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Indian Conference on Bioinformatics 2023 -
Inbix'23
THEME : Application of Bioinformatics in Healthcare
November 24-26, 2023
BOOK OF
ABSTRACTS
Organized by
SCHOOL OF BIO SCIENCES AND TECHNOLOGY
In association with
Inbix'23
Indian Conference on Bioinformatics 2023 - Inbix'23
School of Bio Sciences and Technology in association with Bioclues Organization
Indian Conference on Bioinformatics 2023 - Inbix'23
THEME: Application of Bioinformatics in Healthcare
November 24-26, 2023
Book of Abstracts
Organized by
SCHOOL OF BIO SCIENCES AND TECHNOLOGY
In association with
Indian Conference on Bioinformatics 2023 - Inbix'23
School of Bio Sciences and Technology in association with Bioclues Organization
ORGANIZING COMMITTEE
Chief Patron
Dr. G. Viswanathan, Founder & Chancellor, VIT
Patrons
Mr. Sankar Viswanathan, Vice President, VIT
Dr. Sekar Viswanathan, Vice President, VIT
Dr. G. V. Selvam, Vice President, VIT
Co-Patrons
Dr. V. S. Kanchana Bhaaskaran, Vice-Chancellor-in-Charge, VIT
Dr. Partha Sharathi Mallick, Pro-Vice Chancellor, VIT
Dr. T. Jayabarathi, Registrar, VIT
Organising Chairs
Dr. R. Siva, Dean, SBST, VIT
Dr. Jayaraman K Valadi, Distinguished Professor, FLAME
University, Pune and Adviser-In-Chief, Bioclues.Org
Co- Chairs
Dr. Jayanthi Abraham, Associate Dean, SBST, VIT
Dr. Gyaneshwar Chaubey, President, Bioclues.Org
Convenor
Dr. Vino S, Professor, SBST, VIT
Dr. K Sri Manjari, GenepoweRx, Secretary, Bioclues.Org
Organising
Secretaries
Dr. Sajitha Lulu S, Associate Professor, SBST, VIT
Dr. Anshuman Dixit, ILS, Bhubaneshwar
Dr. Anil Kumar S, Outreach Chair, Bioclues.Org
BIRD Award Chairs
Dr. Pushpendra Singh, Research Chair, Bioclues.Org
Dr. Prashanth N Suravajhala, Founder, Bioclues.Org
Program Committee
Advisers
Dr. P. B. Kavi Kishor, Professor Emeritus, Adviser, Bioclues.Org
Dr. Nirmal K Lohiya, Professor Emeritus, Adviser, Bioclues.Org
Dr. Anshuman Dixit, Principal Scientist, ILS, Bhubaneshwar
Dr. V.S. Sundararajan, Professor Emeritus, Adviser, Bioclues.Org
Dr. Arnold Emerson I, Professor, VIT
Dr. Venkat Kumar S, Professor, VIT
Dr. Rameshpathy M, Professor, VIT
Dr. Devi Rajeshwari V, Associate Professor, VIT
Outreach Chairs
Dr. Indrani Biswas, Joint Secretary, Bioclues.Org
Dr. Rajul Jain, Joint Secretary, Bioclues.Org
Local organizing
Dr. Pragasam V, Professor, SBST, VIT
Indian Conference on Bioinformatics 2023 - Inbix'23
School of Bio Sciences and Technology in association with Bioclues Organization
Committee
Dr. Jayaraman G, Professor, SBST, VIT
Dr. Gothandam K M, Professor, SBST, VIT
Dr. Ramanathan K, Professor, SBST, VIT
Dr. Rajasekaran C, Professor, SBST, VIT
Dr. Shanthi V, Professor, SBST, VIT
Dr. Asha Devi S, Professor, SBST, VIT
Dr. Mohanapriya A, Associate Professor, SBST, VIT
Dr. Subathra Devi C, Associate Professor, SBST, VIT
Dr. Abilash V G, Associate Professor, SBST, VIT
Dr. Suthindhiran K, Associate Professor, SBST, VIT
Dr. Sudhakaran R, Associate Professor, SBST, VIT
Dr. Gayathri M, Associate Professor, SBST, VIT
Dr. Nalini E, Assistant Professor, SBST, VIT
Student Coordinators
Ms. Malavika J
Ms. Kavya P
Ms. Anagha Renjitha
Ms. Goldy Singh
Ms. Devi Soorya Narayana S
Ms. Tejaswini B
Mr. Premkumar T
Mr. Girishwaran M
T�
(VIT)
Dr. Partha Sharathi Mallick
Pro-Vice Cancellor
MESSAGE
I am delighted that the School of Bio Sciences and Technology (SBST) will host the Indian
Conference on Bioinformatics, Inbix'23, during November 24-26, 2023, in collaboration with
Bioclues Organization, India's largest Bioinformatics Society that hosts the Inbix series of
conferences in collaboration with multiple reputed organizations of the country.
I am delighted to read that several notable Professors from prestigious institutes in India and
overseas have been invited to participate in the conference, as have participants from 45
institutions across the country.
I welcome everyone and hope the Inbix'23 a great success.
With Best wishes,
Gyaneshwer Chaubey
President, Bioclues Organization
Message
The Bioclues probably stands as South Asia's largest non-clinical Biological society, boasting 9400 members (414
life members). What draws such a significant following? The dedicated core members and, of course, the field of
Bioinformatics, which itself exhibits boundless qualities- interdisciplinary and pulsating with the vigor to
embrace movements fostering new domains, consistently connecting the threads, and reaching new boundaries.
Attending our annual meetings (INBIX) ensures exposure to state-of-the-art research, instills excitement, and
imparts immediate, applicable knowledge. Moreover, presenting at these yearly conferences offers the chance to
connect with numerous individuals and share one's research. I attribute these expectations to our annual
gatherings' unrestricted and open atmosphere. Regardless of age or professional standing, participants engage in
uninhibited discussions and occasionally vigorous debates in the universal language of science. Alongside the
Organizing Committee, we aim to nurture and promote this sense of exhilaration and stay at the forefront.
Our society has addressed research ethics issues previously considered taboo and challenging to confront
directly. While recognizing the diversity of opinions on these matters, I firmly believe that the educational
initiatives for young scientists, particularly in training on handling statistics and data appropriately, hold
significant implications for the future of Science. The society has undertaken a distinctive initiative involving
Bioinformatics education for school students. Over 100 lectures have been conducted online and offline,
supported by the volunteer spirit of our members. Engaging in such grassroots activities sets our academic
society apart, as such endeavors are relatively uncommon. Our commitment extends to other activities, including
collaborative efforts for internationalization with overseas educational institutions. I extend my heartfelt
gratitude to all those who have actively participated in our society, contributing to these endeavors passionately.
India lags in gender equality, securing the 127th position out of 146 countries in the Global Gender Gap Index
2023. While actively spearheading measures to combat this issue in our country, we should recognize the
formidable challenge of achieving immediate improvement. This realization underscores the need for sustained
and dedicated efforts to reinforce the positive trends that numerous individuals in our society have
conscientiously pursued. Beyond gender and LGBTQ+ concerns, diversity encompasses broader dimensions such
as nationality and regional disparities. We seek collaborative brainstorming and convergence on potential actions
an academic society can take to collectively address these multifaceted diversity issues.
The Bioclues Society aims to keep exciting science at its core while actively addressing challenges, fostering an
inclusive environment, and promoting diversity in Bioinformatics. Members are encouraged to contribute their
opinions and advice to shape the future direction of the society.
In the end, I congratulate the honorable Dean and the organizing committee of VIT for hosting INBIX2023! During
this congress, maintaining a foundation rooted in captivating scientific pursuits, we aspire to address diverse
challenges with your valuable guidance and counsel.
Yours in Bioclues
Gyaneshwer Chaubey
Dr. K. Sri Manjari
Secretary,Bioclues Organization.
Bioclues, an acronym for BIOinformatics CLUb for Experimenting Scientists, has emerged as a
pivotal force in the realm of bioinformatics, especially within the Indian scientific community.
Established in 2005, this non-profit virtual organization has grown into one of the fastest-
growing bioinformatics societies in India, boasting a membership of over 4600 individuals
from nearly 30 countries.
In a landscape where bioinformatics has flourished with the mantra of 'sequence predicts
structure predicts function,' the early bioinformaticists in India were primarily computational
chemists and cell biologists exploring molecular modeling and protein function prediction. The
need for a society focused on mentoring, outreach, research, and entrepreneurship became
evident with a surge in bioinformatics programs at both undergraduate and graduate levels.
This led to the inception of Bioclues in 2005.
Bioclues operates with a focus on four key avenues: Mentoring, Outreach, Research, and
Entrepreneurship (MORE). These avenues represent the pillars of Bioclues' mission, guiding its
efforts to foster a vibrant and collaborative bioinformatics community. These four avenues
collectively form the foundation of Bioclues' activities and initiatives. The organization's
commitment to MORE reflects its holistic approach to building a strong and dynamic
bioinformatics community that excels in academic and research pursuits and embraces
practical applications and real-world challenges.
Bioclues' commitment to excellence extends beyond national borders, as evidenced by its
diverse membership and collaborations with international universities. The society's emphasis
on open access and its role in bridging the gap between real-time professionals and academics
highlight its relevance in the dynamic field of bioinformatics.
Bioclues stands as a testament to the collaborative spirit of bioinformaticians. Overcoming
challenges, fostering innovation, and nurturing the next generation of scientists, Bioclues
continues to play a vital role in shaping the future of bioinformatics in India and beyond.
As Bioclues steps into its 19th year of service in October 2023, the organization has set its
sights on Vision 2030. Having successfully organized conferences across various locations,
including Jaipur, Jalandhar, Shillong, and Guntur, the society remains committed to fostering
interactions and collaborations among practitioners. The upcoming Indian Conference on
Bioinformatics 2023 - Inbix'23, organized by Bioclues and Vellore Institute of Technology,
signifies a continued dedication to providing a platform for researchers to engage with top
professionals in the field. We eagerly anticipate engaging in future endeavors with VIT and
Bioclues, fostering further successful collaborations.
K. Sri Manjari (PhD)
Secretary, Bioclues
INDEX
S. No
Title
Page. No
Key Note
1.
How Can We Interpret the Genome Language?
1
LTA
2.
Historical Evolution of Protein Function Annotation Methods
2
3.
Functional Genomic Approaches to Breed Sorghum bicolor (L.)
Moench, the Great Millet for Climate Resilience
3
4.
Transcriptomics and Epigenetics of Leaf Rust Resistance in Wheat
4
Invited Talks
5.
Decoding The Hypes and Hopes of Drug Discovery and Precision
Medicine in Schizophrenia: A Genomic and Epigenomic
Perspective
5
6.
Pistachio Adaptation to Salt Stress as Revealed by Physiological
and Proteomics Studies
6
7.
Tools for Molecular Design and Activity Profiling; A Reductionist
Approach
8
8.
Newborn Screening by Integration of Metabolomics and
Genomics for Second -Tier Analysis
9
9.
Can Glycan Alphabet Provide Clues to Strain-Level Variations in
Gut Microbiome?
11
10.
Human Housekeeping Cis-Regulatory Elements and Their
Involvement in Tumor Suppression
12
11.
Advances in Plant Phenotyping for Climate Smart Agriculture:
Applications of Hyperspectral Imaging
13
12.
Millets for Millenium
15
13.
Model-Informed Drug Discovery and Development: From
Bioinformatics to Quantitative Systems Pharmacology (QSP)
16
14.
Journey of Developing Reversible Inhibition of Sperm Under
Guidance (Risug®) a
s an Injectable Male Contraceptive with
Special Reference to Seminal Proteomics
17
15.
Candida Sterol 14α-demethylase – Patterns of Amino Acid
Substitutions and Azole Resistance
19
16.
Zebrafish as a Powerful Model System to Investigate Cellular and
Developmental Mechanisms
20
17.
New Drug Development - Lab to Launch (An Industry
Perspective)
21
18.
Bioinformatics Applications for Solving the Mystery of Complex
Human Diseases: An Aid in Healthcare
22
19.
Bioprospecting of Halotolerant Microorganisms by Metabolomic
Approach
23
20.
In Silico Secretome Prediction and Expression Analysis of
Potential
Effector Candidates of Fall Armyworm (Spodoptera
frugiperda)
24
21.
Biogenic Iron Oxide Nanoparticles and CRISPR as a Panacea for
Combatting Global Warming
25
22.
A Computational Study of Conformational Transitions in
Intrinsically Disordered Regions on Complexation
27
23.
Design and Validation of CRISPR-Cas13a-Based Tool for
Detection of K. pneumoniae
29
Oral Presentation
24.
MicroRNAs and Gene Expression Analysis for Their Regulatory
Role in Alzheimer’s Disease
31
25.
Ancient Antimicrobial Resistance Genes Unearthed: Insights from
Pleistocene Permafrost and Ice Core Metagenomes
34
26.
Exploring Antimicrobial Compounds in Streptomyces strain
VITGV100 (MCC 4961) with Chemical Elicitor
36
27.
Network Pharmacology Based Study on The Mechanism of Aloe
Ve r a for Treating Psoriasis
37
28.
Targeting Mitochondrial Dynamics: An In-silico Approach for
Repurposing Antifungal Drugs in OSCC Treatment
38
29.
Stratifying Breast Cancer Subtypes Using DNA Methylation
Markers
42
30.
Renal Sensing of Gut Microbiota Derived- Metabolites in Diabetic
Chronic Kidney Disease: An Integrated Approach Using Network
Pharmacology and Molecular Docking
43
31.
Prediction, Design, Molecular Docking and Dynamics Simulation
of Novel Antimicrobial Peptides from Aegle marmelos against
Staphylococcus aureus
45
32.
Structure Activity Relationship Studies of Anacardic Acid
Derivatives: Implications in Cancer Biology
48
33.
Computational Resources for Understanding and Predicting the
Binding Affinity of Protein- Nucleic Acid Complexes
49
34.
Tackling Drug Resistance in Glioma by Targeting mIDH2R140Q
Protein: A Computational Repositioning Strategy
51
35.
Drug Repurposing Strategies for the Management of Triple
Negative Breast Cancer: Focus on Indoleamine 2, 3-Dioxygenase
and Tryptophan-2, 3 Dioxygenase Targets
53
36.
In Silico Design of Antimicrobial Peptides from Bungarus
Caeruleus and Molecular Docking & Dynamics Simulation
against Mycobacterium Tuberculosis
55
37.
In-Silico Approach to Explore Anticancer Properties in Gloriosa
Superba Derived Compounds against Prostate Cancer
57
38.
Proteome-Wide Scanning Approach to Detect rpIE as a Novel
Therapeutic Target of M.Ulcerans
58
39.
Computational Identification of Biomarker Genes for Hormone-
Sensitive Cancers Considering Treatment and Non-Treatment
Studies – A Meta-Analysis Approach
59
40.
Investigation of the Impact of R273H and R273C Mutations on the
DNA Binding Domain of P53 Protein through Molecular Dynamic
Simulation
60
41.
Evaluation of Ocimum Basilicum for its Antifibrotic and Drug-
Likeness properties – a Computational Pharmacology Approach
61
42.
Systems and Computational Screening identifies SRC and
NKIRAS2 as Baseline Correlates of Risk (CoR) for Live
Attenuated Oral Typhoid Vaccine (TY21a) induced Protection: An
in silico pipeline
62
43.
Association of CTLA-4 signal peptide (T17A) Polymorphism with
rheumatoid arthritis in the Indian population: A case-control
study and In silico analysis
64
44.
Logical Modelling of Gene Regulatory Circuits involved in CCL20
Induction in Human Organoids using Systems and Computational
Analysis of RNA Seq dataset
66
45.
Does Metabacillus halosaccharovorans possess inherent
radiation resistance? A comparative genome approach
68
46.
A multi-objective hybrid machine learning approach- based
optimization for enhanced biomass and bioactive fucoxanthin
production in Isochrysis galbana
70
47.
Phylogenetic status of a field crab (Brachyura: Decapoda) from
Pedavedu, Thiruvannamalai (Tamil Nadu): an integrative
approach through molecular taxonomy, barcoding and coding
matrix
72
48.
In silico subtractive proteomics analysis to identify the novel
therapeutic drug targets combating antibiotic resistance in
Neisseria gonorrhoeae.
73
49.
Zero inflated Conway-Maxwell Poisson model: An application to
cross-sectional Microbiome data
75
50.
Insight into the bacterial gut microbiome of Penaeus vannamei fed
with functional feed additives Lactiplantibacillus plantarum by
amplicon sequencing
76
Poster Presentation
51.
Evaluating the Performance of Machine Learning Methods for
Predicting Mortality in Intensive Care Unit Patients
78
52.
Structure-Based Drug Designing Towards the Identification of
Potential Anti-Bacterial f
or Acinetobacter Baumannii by
Targeting Penicillin-Binding Protein
79
53.
Immunoinformatics aided designing of a next generation poly-
epitope vaccine against
Pseudomonas aeruginosa targeting
Needle tip protein
80
54.
Unlocking the Molecular Landscape of Hepatocellular
Carcinoma Arising from Non-
Alcoholic Fatty Liver Disease:
WGCNA and Multi-Omics approach.
81
55.
Genomic Investigation of the Heat Shock Transcription Factor
Gene Family Leveraging the Secrets of Drought Resistance in
Black Pepper
83
56.
Role of CTC1 and DNA Repair proteins at the telomere in cancer.
84
57.
Mutational profiling of PPARGC1A and its role in Diabetes,
Obesity and Cancer
85
58.
Establishing a Computational Screening Framework to Identify
Environmental Exposures Using Untargeted GC-HRMS
86
59.
Triphala-induced Oxacillin Sensitivity of Methicillin Resistant
Staphylococcus aureus Mu50 strain: Insights from in silico Studies
87
60.
In silico determination of multidrug-resistant (MDR) genes in
2023 sequenced klebsiella pneumonia’s genome
88
61.
RNA-seq and sRNA-seq analysis in Black Pepper reveals potential
regulatory transcripts in drought tolerance
89
62.
Design of a Multi-Epitope Based Vaccine using Spike
Glycoprotein for Effective Protection Against COVID-19 through
Bioinformatics Approach
90
63.
Gut Metagenomic Analysis of Gastric Cancer Patients Reveals
Akkermansia, Gammaproteobacteria, Veillonella Microbiota as
Potential Non-invasive Biomarkers
91
64.
In Silico Predictive Homology Modeling of PKHD-1 Protein: A
Comparative Study among Three Different Species
92
65.
Genetic Variations and their Impact on Diabetic Retinopathy
Pathogenesis: A Genomic Evolution Perspective
93
66.
Machine learning for T-cell epitope prediction
94
67.
Bioinformatics for drug discovery against drug resistant Candida
species
95
68.
3’UTR SNP rs4709267 associated with rheumatoid arthritis risk
and enhances TAGAP gene expression
96
69.
Studies on microRNAs and their target genes through expression
analysis in Breast Cancer
98
70.
Nephroprotective role of Syzygium cumini ZnO nanoparticles in
streptozotocin‐induced diabetic nephropathy rats
101
71.
Identification of potential phytochemicals as inhibitors of CXCR2
and CXCR4 in Glioblastoma using Molecular docking and
Molecular Dynamic simulation studies.
102
72.
Docking and molecular dynamics simulation revealed the
potential anti-
oncogenic activity of sesamolin in breast cancer
therapeutics targeting the E2F8, a cell-surface receptor protein.
103
73.
An insilico study on analysis of the interaction between
microplastics and aquatic pathogens
105
74.
A Novel Cuproptosis related miRNAs to predict the prognosis of
Renal Cell Carcinoma
106
75.
Immunoinformatics-based Vaccine candidate development for
Edwardsiellosis
108
76.
Comparative transcriptome analysis reveals insights into the
growth traits of Penaeus monodon
109
77.
Prediction of Antimicrobial Resistance Profiles from Salmonella
Variants using ML and DL Approaches.
110
78.
Validation of Sequences from Protease Producing Bacteria with
Unknown Sequences
112
79.
Drug Repurposing for Non-Alcoholic and Alcoholic Fatty Liver
Diseases based on Omics data
113
80.
Drug repurposing analysis for Psoriatic arthritis using PheWAS
and transcriptome data
115
81.
Multi-omics analysis to unravel the molecular etiology of
migraine-related psychiatric disorders
117
82.
Molecular Docking and Molecular Dynamic Simulation (MDS)
Investigation of Actinobacterial based Bioactive Compounds
against Fusobacterium nucleatum aggravated Oral Squamous
Cell Carcinoma (OSCC)
119
83.
Computational Screening and Docking analysis of
Phytochemicals from Senna auriculata and Trachyspermum
copticum against Multiple targets of Mycobacterium tuberculosis
121
84.
Exploration of Phytochemicals as Prospective Drug Candidates
for Tuberculosis: A Computational Approach
123
85.
To identify a novel target compound to inhibit the OXA β-
Lactamases causing extremely drug-resistant hospital-acquired
infections.
125
86.
Differential Gene Expression Analysis to Unveil Potential Targets
and Pathways in Atherosclerosis Under Prolonged Hyperglycemic
Conditions: A Bioinformatics Study
127
87.
Antiproliferative activity of prodigiosin derived from Serratia
marcescens VITSD2: An in vitro & in silico approach.
129
88.
Virtual screening, Molecular Docking and Dynamics Simulations
for identifying potential natural inhibitors for managing
Colorectal cancer against PDZ domain-containing protein GIPC2
130
89.
An integrated bioinformatic approach to identify potential genes
in Colorectal Cancer
133
90.
Flux balance analysis of exopolysaccharide biosynthesis in
Methylobacterium mesophilicum
134
91.
Evaluation and Benchmarking of de novo Assembly Tools for
Prokaryotic Long Reads from Oxford Nanopore Technologies.
135
92.
Targeting Active PA K 1 and Γh2ax to Evade Radiation Resistance
in Head and Neck Squamous Cell Carcinoma
137
93.
Unlocking New Avenues: Exploring Azoles, Statins, and Anti-
cancerous Compounds Against Mucormycosis for Drug
Repurposing through Molecular Docking
139
94.
A Bio Inspired Sentiment Analysis of social media for Early
Detection of Negative Emotions in Young Adults
141
95.
Insights into the Role of Potassium Channels in Migraine
142
96.
Identification of CYP51 from Leishmania major, strain Friedlin as
a potential drug target and repurposing of FDA approved drugs
against it to uncover drug against it.
144
97.
Unmasking Alzheimer's Central Players: A Network Perspective
146
98.
Machine Learning Heuristics for Oral Cancer Datasets to
Ascertain Pathogenesis
148
99.
Exploration of Cannabis Constituents as Potential Inhibitors
Against Gamma-
Secretase to Manage Alzheimer’s Disease: A
Structural Bioinformatics Approach
149
100.
An immunoinformatic approach to assess the cross reactivity of
Indian cobra venom to viper venom
151
101.
Waste Segregation using Deep Learning Neural Network.
153
102.
Unlocking Insights in CNS Cancer through Metabolomics and
Multi-Omics Integration
154
103.
Comparative Atomistic Insights on Apo and ATP-I1171N/S/T in
Nonsmall-Cell Lung Cancer
156
104.
L. acidophilus MV hampers the gtfB enzyme and impedes the
formation of dental plaque by S. mutans: an in vitro and in silico
approach
158
105.
Exploring different traits of the isolate Priestia megaterium VIT
for maintaining equilibrium in energy expenditure between
systemic resistance and plant growth
160
106.
Assessment of Anti-inflammatory potentials of Rosa indica
162
107.
Transcriptomic analysis reveal Tissue-specific Gene Regulatory
Circuits associated with Systemic Lupus Erythematosus: A
Systems and Computational Study
164
108.
Insilico screening of phytochemicals from Indian medicinal plants
for the
identification of potential antibacterial activity against
Mycoplasma penetrans
166
109.
Comparative analysis of physicochemical features of structured
and disordered proteins in humans
168
110.
Identification of the common differentiating genes among various
cancers on the basis of RNA expression
169
111.
In silico analysis of atropine as a potential inhibitor of NS5 protein
of Japanese encephalitis virus
170
112.
Computational Analysis of Structure and Function of Siglec1 and
Prediction of
Potential Pharmaceutical Agents for Rheumatoid
Arthritis
171
113.
Transcriptomic analysis of stress response pathway in HEK293
cells during recombinant AAV production
172
114.
Single nuclei transcriptomics datasets of brain tissue analysis by
in-house integrated cross species platform
174
115.
Investigation of Phe-tRNA interaction with EF-Tu in GDP/GTP
nucleotide bound states: A molecular dynamics simulation study.
176
116.
Comprehensive analysis of amyotrophic lateral sclerosis gene
expression data to ascertain candidate biomarkers
178
117.
Isolation, Characterization, and Genome Analysis of Lytic
Bacteriophage Vb_SalP_1 against Food-
borne Pathogen
Salmonella enterica
179
118.
Exploring Salidroside and its Derivative Compounds as Potential
DAPK1 Inhibitors: An In Silico Study for Reducing Alzheimer's
Disease Risk
181
119.
In-silico identification of the potent carotenoids targeting the
kinase receptors for cancer therapy
182
120.
Cigarette Butts: Tiny Nocuous particles inciting ecotoxicity via
oxidative stress in Pila virens
184
121.
Molecular Docking Study of Anti-Arthritic and Anti-Oxidant
Phytochemicals Identified from Indian Medicinal Plants Against
RANKL In the Treatment of Bone Degradation in Rheumatoid
Arthritis
185
122.
Bioaugmentation Of Phenanthrene- Polycyclic Aromatic
Hydrocarbon Using Sphingomonas Species Isolated from The
Petrol Bunk Soil
187
123.
Nicotine Deter Women’s Reproductive Health: A Molecular
Docking and Dynamic Simulation Studies
189
124.
Decoding the conformational impact of PTEN mutant R130Q on
binding to the PIP2 enriched cell membrane in breast cancer: A
computational approach
190
125.
In silico analysis of metabolites produced by lactic acid bacterial
(LAB) cultures against Aquatic pathogens
191
BIXS Talks
126.
Nipah Henipavirus Proteins Database
192
127.
SCVDB: A Database of SARS-CoV-2 Annotated Proteins
194
Video Abstracts
128.
In Silico Analyses of Protein-Ligand Interactions Associated with
Stress Pathways in Plants
196
129.
Unraveling the Genetic Basis of Prostate Cancer Phenotype in the
Indian Population
197
130.
Comparative Analysis of RNA-Seq Results in Parkinson's Disease:
Unraveling Molecular Insights into Disease Pathogenesis
199
131.
Repurposing Targeted Therapeutics Against Monkeypox: In Silico
Modeling for the Upcoming Battle
200
132.
Matrix Metalloproteinases: Master Regulators of Tissue
Morphogenesis
201
133.
Unraveling the Clotting Cascade in Vitamin K Deficiency
Associated Comorbidities
202
134.
Inferring Variants of Uncertain Significance (VUS) in Rare
Disease Genetics: An India-Centric Study
203
135.
Inferring Recombination Events in SARS-CoV-2 Variants In Silico
205
136.
Applications of Explainable AI in Bioinformatics
207
137.
Development of a Web-Resource for Prioritizing Mutations from
Next Generation Sequencing Data Using an Agnostic Framework
209
138.
A Holistic Perspective on Host-Pathogen Interactions in Different
COVID-19 Severity Levels Through an Integrated Omics Analysis
211
139.
Semantic Retrieval of Antimicrobial Resistance Information using
Natural Language Processing and Deep Learning
212
140.
AI-Generated Synthetic Genes for Investigating Novel Biomarkers
of Systemic Lupus Erythematosus
213
Indian Conference on Bioinformatics 2023 - Inbix'23
1
Keynote
How Can We Interpret the Genome Language?
Kenta Nakai, The Institute of Medical Science, the University of Tokyo, Japan
knakai@ims.u-tokyo.ac.jp
Abstract
It is often said that the genome is a blueprint of life written in an unknown language (i.e., the
genome language). In this analogy, genes may be regarded as sentences and motifs as words. To
master a language, we need a dictionary and a grammar book. In biology, dictionaries correspond
to databases, and thus databases containing known transcription factor binding sites have been
constructed. However, if we use such a database to interpret an unknown genome sequence, we
instantly face the problem of many false-positive hits. Thus, motifs must be interpreted in an
appropriate context, though there must be various molecular reasons underlying such contextual
effects. In other words, an appropriate grammar book is needed. In contrast to dictionaries,
however, it is not obvious how to organize a grammar book in biology. It may be constructed as
a probabilistic model, like hidden Markov models, or a rule-based knowledge base. Recently,
there have been great advances in the field of natural language processing. Surprisingly, such an
approach, the large language model (LLM) approach, seems to be rather useful in interpreting
the genome language, too. Then, maybe we can understand what contexts are important by
reverse-engineering which positions in the genome those LLMs learned important to pay
attention to. In this talk, I will explain what I have tried so far with the above thoughts as well as
our recent efforts to interpret the grammar of enhancers and RNA splice sites.
Indian Conference on Bioinformatics 2023 - Inbix'23
2
LTA1
Historical Evolution of Protein Function Annotation Methods
Jayaraman Valadi, FLAME UNiversity
jayaraman.vk@flame.edu.in
Abstract
Overwhelmed with recent advances in sequencing methodologies, the volume of pure sequence
and structure data along with its diversity are growing rapidly. Fueled by this, newer function
annotation methods and paradigms are evolving continuously. In this lecture I will briefly outline
the historical evolution of Machine learning and Deep learning-based Protein function annotation
methods.
Indian Conference on Bioinformatics 2023 - Inbix'23
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LTA2
Functional Genomic Approaches to Breed Sorghum bicolor (L.)
Moench, the Great Millet for Climate Resilience
Kavi Kishor P B (Department of Genetics, Osmania University, Hyderabad, 500 007).
pbkavi@yahoo.com
Abstract
Sorghum bicolor or jowar is also known as great millet and used as a staple food in Asia including
India, Africa and Middle East countries. It is used as a fodder and an important source for the
production of ethanol (biofuel). It is rich in many phenolic compounds and anthocyanins that can
act as antioxidants and hence prevent cancer. S. bicolor has been shown to reduce inflammation
which could be due to the presence of anthocyanins and phenolics. It is gluten-free millet with
high fiber, being used for weight loss and also preferred by the diabetics. It is claimed that it is
safe for humans suffering from celiac disease and gluten intolerance. Ever increasing population
coupled with climate change and water scarcity are the major threats to our food and nutritional
security in future. This needs to be addressed on war footing utilizing our current understanding
of the gene function identification, and genome-editing technologies. The genomic sequence of
S. bicolor is known, but the functional validation of many of its genes is not yet over. Functional
validation of the genes by overexpression’s or suppressions will help to identify candidate genes
that can be deployed in future in breeding programs of S. bicolor aimed at its superior agronomic
performance under saline, water and high temperature stress conditions. In the present study,
genes implicated in salt, drought and temperature stresses such as sodium porters, potassium
porters, sodium porter-like proteins (SbNHXLP), and transcription factors like SbAP37 have
been identified and validated. The details will be discussed during the presentation.
Indian Conference on Bioinformatics 2023 - Inbix'23
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LTA3
Transcriptomics and Epigenetics of Leaf Rust Resistance in Wheat
Pushpendra K Gupta (CCS University, Meerut)
pkgupta36@gmail.com
Abstract
Transcriptomics and epigenetic modifications in wheat involving resistance against leaf rust due
to two different genes, namely Lr28 for seedling resistance and Lr48 for adult plant resistance
were examined. Using NILs, differentially expressed transcripts (DETs) and the sequences with
differential epigenetic modifications (DNA methylation, histone modifications and ncRNAs)
were identified. Methylation of DNA was studied using MASP, ChIP-PCR, MeDIP and BiS-Seq,
histone modifications were examined using ChIP-PCR and ChIp-Seq and ncRNAs were
identified using RNA sequencing. In transcriptome analysis >100 genes were found to be
differentially expressed in resistant and susceptible NILs. Transcriptome data was also utilized
for identification of few putative effector molecules of the pathogen. In susceptible line, a large
number of genes were activated due to hypomethylation and fewer genes were repressed due to
hypermethylation, suggesting that many genes that are active in S cultivar are silenced in R NIL.
Among differentially expressed genes, two genes encoding N-acetyltransferase and peroxidase12
(examined using ChIP-PCR), largely matched with changes in H3K4/H3K9 acetylation patterns
of their promoter regions. Methylation context was also important, such that mCG methylation
was abundant in S cultivar and that of mCCG methylation in R NIL. Similarly, methylation of
CHH, which is generally uncommon, was found to be abundant among differentially methylated
regions. Among different regions of the genes also, level of methylation was generally abundant
in intergenic regions followed by that in promoters, transcription termination sites (TTSs) and
exons/introns. Using RNA-seq data, a number of miRNAs and lncRNAs were also identified to
be differentially expressed.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Invited Talks
Decoding The Hypes and Hopes of Drug Discovery and Precision
Medicine in Schizophrenia: A Genomic and Epigenomic Perspective
Moinak Banerjee (DBT-Rajiv Gandhi Center for Biotechnology)
moinak@gmail.com
Abstract
Schizophrenia is known to be influenced by both gene and environment. Majority of the drugs
used to treat target the neurotransmitters. Based on this hypothesis several investigators had
focused on genetics and epigenetics of neurotransmission in Schizophrenia. We wonder are
altered neurotransmission the real targets of disease pathogenesis. In epigenetics DNA
methylation, histone modifications and microRNAs are the crucial molecular signatures for
determining the epigenetic influence. Several reports suggest differences in the pattern of DNA
methylation both at global and gene specific level in Schizophrenia. However, many of these
observations could not be replicated unanimously. The reason could be due to environmental
differences, medication effects or ethnic (genomic) differences. Besides most of the studies have
considered the genetic and epigenetic events as two independent events. None of these studies
have implied the role of genomics of methylome in Schizophrenia. We demonstrated that the
DNMTs which are responsible for maintaining the methylations, are themselves associated with
Schizophrenia which might possibly explain the discrepancies in global or gene specific
methylations. Methylation pathway can also be influenced by folate cycle, methionine cycle and
transsulfuration cycle genes. Therefore, in continuation to methyltransferases which are regulated
intrinsically, we also find that the methylation can also be influenced by extrinsically modulated
genes. Majority of the studies have investigated on the role of epigenetics from the perspective
of Schizophrenia pathogenesis but none of them have investigated whether these epigenetic
changes could also be induced by the therapeutic drugs itself. This compelled us to identify if
antipsychotic drugs can impact the host epigenome and if so, how does it impact treatment
response. We evaluated the epigenetic response of the drugs using 850K genome wide
methylation, 60K genome wide miRNA screening. Interestingly we observe similar direction for
methylation and miRNA signature, which indicate that the epigenetic observation needs a careful
evaluation in pathogenesis and how it impacts treatment response.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Pistachio Adaptation to Salt Stress as Revealed by Physiological and
Proteomics Studies
Ramesh Katam (Florida A&M University), Mohammad Akbari (Pistat Research Center, Nazari
Business Group), Rakesh Singh (Georgia Institute of Technology), Dalia Vishnudasan (Amrita
School of Biotechnology, Amrita Vishwa Vidhyapeetham) and Elena Andriunaite (Lithuanian
Research Centre for Agriculture and Forestry).
ramesh.katam@famu.edu
Abstract
Pistachio (Pistacia vera L.) is an economically important tree nut that commonly thrives in semi-
arid and arid environments. P. vera is a highly adaptable to various abiotic stresses, and it can
tolerate drought and salinity stresses, which makes it suitable for reforestation of arid and salinized
zones. However, the mechanisms underlying the salinity tolerance of this plant are not well
understood. The present study was aimed at physiological and molecular investigations to unravel
the metabolic pathways associated with the salt tolerance mechanisms in UCB-1 cultivar. Five
one-year-old pistachio rootstocks were treated with four saline water regimes for 100 days. The
rootstocks adopted Na+ exclusion strategy to resist the salinity stress. Total proteins were isolated
from the roots and treated with different NaCl concentrations. The proteins were characterized
using high throughput LC-MS/MS spectrometry searched against the Citrus database. Over 1600
protein IDs were detected, among which the comparative analysis revealed 245 more abundant
and 190 low abundant proteins to three stress levels. The proteins associated with amino acid
metabolism, cell wall organization, protein homeostasis, response to stress, signal transduction,
TCA cycle, and vesicular trafficking were constantly over expressed at all stress levels. At low
and moderate stress levels, the chromatin and cytoskeleton organization lipid metabolism proteins
were over expressed, while at higher salt concentrations, they were unaffected. Transcription and
translation processes were affected by all stress levels, as the proteins showed down-regulation in
response to all stress levels. Transcription proteins were downregulated at low and moderate stress
while over expressed at high salt stress treatment. Protein interaction network with all the
orthologous proteins mapped to Arabidopsis thaliana and the clusters associated with these
proteins revealed the cytosolic, carbohydrate, and amino acid metabolism are associated with
Indian Conference on Bioinformatics 2023 - Inbix'23
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salinity stress tolerance. The proteome data were validated with corresponding changes in
transcripts.
Indian Conference on Bioinformatics 2023 - Inbix'23
8
Tools for Molecular Design and Activity Profiling: A Reductionist
Approach
Vibin Ramakrishnan, Indian Institute of Technology Guwahati
vibin@iitg.ac.in
Abstract
Translation of basic science to novel diagnostic and therapeutic solutions for prevention,
diagnosis and treatment of diseases is an exciting area of research. This process however, is
complex in its design and execution. Here we present a blend of three computational and
experimental methodologies that can be employed in the design and profiling of functional
molecules in the early stages of the drug discovery.
In the first part, we discuss the prospects of a reductionist approach in converting protein structure
to a Barcode. In the second, a ‘clock model’, for virtual activity profiling of drug candidates will
be discussed. This algorithm may be extended to drug promiscuity and supplementing fragment-
based drug design efforts.
In the third part, we present the design and development of a minimal blue fluorescent protein.
We have employed two design tools developed by us; AR-SAMD and IDeAS. This molecule to
the best of our knowledge, is the first blue fluorescent artificial protein designed with diversified
chain stereochemistry.
Indian Conference on Bioinformatics 2023 - Inbix'23
9
Newborn Screening by Integration of Metabolomics and Genomics for
Second-Tier Analysis
Sunil Kumar Polipalli, MAMC & Associated Lok Nayak Hospital
sunilpkumar18@gmail.com
Abstract
Newborn screening for treatable “hidden” hereditary metabolic disorders was introduced almost
60 years ago. Since, its inception in 1963, newborn screening has traveled a part from a targeted
analyte assay to a program of immense public health. From inception, being a microbiologic test
conducted on Guthrie paper, it currently uses fluoroimmunoassay and liquid chromatography
mass spectrometry. As envisaged originally, in context to a good public health program, it is
designed to have a good sensitivity at a trade off with tolerable specificity. As a result, all the
analytes that do not fall into a definite presumptive positive flagging but may suggest a
series/cause of IEM. As a protocol despite repunch from another spot recalling the infant for an
ambiguous diagnosis, brings with it both the emotional trauma and anxiety besides additional
costs for the program. Last 2 decades have seen the emergence of second tier biochemical
screening on the residual blood spots to increase the positive predictive value of these analytes.
The performance matrix has improved by adding ratios, adding second tier biochemical tests like
alloisoleucine for the diagnosis of Maple syrup urine disease, methyl citric acid for propionic
acidemia in a neonate with increased propionyl carnitine amongst many others. Genomics in NBS
is emerging not only due to improved techniques of DNA extraction but also the widespread use
of next generation sequencing. Using DNA based technology as a second-tier testing modality
will not only complement its biochemical counterpart but also significantly improve the
specificity of the program. In a country like India, with limited medical literacy, conceptual
understanding of the informed consent form and denial of recall due to cultural taboos makes
second tier genomic testing of immense benefit. There are other challenges that need to be
addressed using second tier genomics. The genetic basis of IEMs is very heterogeneous and can
involve abnormalities such as point mutations, deletions or insertions, or more complex genomic
rearrangements. Introduction of molecular genetics techniques have made it possible to identify
molecular defects and confirm diagnoses in IEMs. However, disease phenotypes are not always
explained
Indian Conference on Bioinformatics 2023 - Inbix'23
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by the detected variants. Indeed, the level of IEM complexity requires an integrated
understanding of perturbations in genetic and biochemical networks. The aim of this study is to
integrate expanded newborn screening using metabolomics and genomics for the second-tier
screening method to assist clinical diagnosis. IEM phenotype characterization potentially
provides the clinician better information for personalized care. In combination with genomics,
large-scale semi targeted metabolomics is expected to reveal genotype-phenotype correlations
and the overall effect of drugs and dietary interventions. However, for the successful translation
of global metabolomics from the bench to bedside, some gaps need to be bridged and quality
control challenges need to be addressed. This will usher a new area where positive predictive
yields will improve without increasing the recall and precision neonatology will improve neonatal
mortality rates with a move towards precision wellness, the aim of this decade.
Indian Conference on Bioinformatics 2023 - Inbix'23
11
Can Glycan Alphabet Provide Clues to Strain-Level Variations in Gut
Microbiome?
Jaya Srivastava (Indian Institute of Technology Bombay) and Petety V Balaji (Indian Institute
of Technology Bombay)
balaji@iitb.ac.in
Abstract
Proteins, nucleic acids, carbohydrates, and lipids are the four major classes of biomolecules. The
alphabets of proteins and nucleic acids have been extensively studied and are found to be well
conserved in evolution. In contrast, characterization of the alphabets of carbohydrates and lipids
has considerably lagged behind due to inherent structural complexity. There are no known
"templates" (vis-a-vis those for proteins and nucleic acids) for the biosynthesis of glycans and
lipids. Our analysis of completely sequenced prokaryotic genomes showed that monosaccharides
may be grouped as common, less common, and rare based on their prevalence in Archaea and
Bacteria. In addition, we found substantial variations in the set of monosaccharides used by
organisms belonging to the same phylum, genera and even species. This can be exploited to
identify strain-level variations in microbial communities.
Indian Conference on Bioinformatics 2023 - Inbix'23
12
Human Housekeeping Cis-Regulatory Elements and Their
Involvement in Tumor Suppression
Martin Loza (The University of Tokyo), Alexis Vandenbon (Kyoto University) and Kenta Nakai
(Institute of Medical Science, University of Tokyo)
knakai@ims.u-tokyo.ac.jp
Abstract
This study explores housekeeping cis-regulatory elements (HK-CREs) in the human genome.
Through extensive multiomics analysis, we highlight the unique epigenetic features of these
elements and explore their importance in vital biological processes beyond the regulation of
housekeeping genes. Notably, we observe reduced activity of HK-CREs in cancer cells,
particularly those near the telomere region of chromosome 19 and associated with zinc finger
genes. Further analysis, including cancer samples, suggests the importance of these genes in
housekeeping tumor suppressor processes. Overall, our findings highlight the importance of HK-
CREs within the cells for preserving cellular integrity and stability.
Indian Conference on Bioinformatics 2023 - Inbix'23
13
Advances in Plant Phenotyping for Climate Smart Agriculture:
Applications of Hyperspectral Imaging
Pawan Kulwal* (Centre for Advanced Agricultural Science and Technology for Climate Smart
Agriculture), Sunil Kadam* (Mahatma Phule Krishi Vidyapeeth, Rahuri, MS), Anjali Pundkar
(Mahatma Phule Krishi Vidyapeeth, Rahuri, MS) and Vishal Pandey (Mahatma Phule Krishi
Vidyapeeth, Rahuri)
pawankulwal@gmail.com, sunil21075@gmail.com
Abstract
Majority of the agriculturally important traits are dependent upon many physiological and
biochemical parameters which are ultimately responsible for biotic and abiotic stresses. However,
these traits are often expensive, destructive or slow to score and also there is large gap between
plant physiology, genetics and phenomics investigations. Moreover, in order to identify the
desirable plants, breeder often needs to take repeated observations in the field and make careful
selection. This not only requires lot of time, but skill and experience of a breeder. It has now been
realized that in any plant breeding program, rapid and precise phenotyping for the desired trait is
very essential. Since this involves recoding thousands of data points in shorter time, in recent
years a shift from traditional way of phenotyping to use of sensor-based phenotyping has been
seen. For instance, digital images or sensor-based images of standing crop in the fields are taken
from the surface or through air with the help of unmanned aerial vehicles. This not only saves
time in recording the data but also reduces the error associated with the manual way of recording
observations. This makes it necessary to use techniques which are high throughput and non-
destructive in nature. Hyperspectral imaging-based canopy reflectance is one of the recent and
promising techniques. The spectral signatures reflected from the plant canopy at different
wavelengths provide different types of information on specific plant characteristics responsible
for phenotyping. The spectral signatures are closely related to biotic and abiotic stress induced
changes in several biochemical and biophysical traits. These can be related to genotypic
differences and stress levels and can be detected through the changes that take place in the spectral
signatures of the canopy measured in the visible, near-infrared, and short wave-infrared regions.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Based on the experiments carried out under controlled as well as field conditions, we observed
that spectral signatures captured using hyperspectral imaging system can efficiently distinguish
tolerant/resistant and sensitive genotypes for temperature stress in wheat as well as leaf blast in
rice. We observed that the canopy spectral signature can efficiently be used for phenotype sensing
for breeding purposes. The amount of high-dimensional data which can be generated in any such
large experiment can effectively be analyzed using the technique of artificial intelligence and can
be used for identification of genes/QTLs governing different traits. This will provide plant
breeders with important information to increase the chances of recognizing genotypes/ varieties
that are well-adapted to biotic and abiotic stresses by identifying indirect non-destructive traits.
Indian Conference on Bioinformatics 2023 - Inbix'23
15
Millets for Millenium
Pawank Kulwal (Vignan University) and Anil Kumar S (VFSTR, Guntur)
pawankulwal@gmail.com
Abstract
The United Nations General Assembly at its 75th session in March 2021 declared 2023
the International Year of Millets (IYM). As part of these celebrations, several events in the form
of workshops, seminars, symposia, brain storming sessions, etc. related to promotion of millets
were organized earlier this year while some more are planned. Millets because of their resilient
ability to grow under arid conditions and with minimal inputs can play an important role in food
and nutritional security.
Discussions taking place as part of IYM at different forums provide an opportunity to create
awareness about several aspects of millet production, processing, consumer preference,
marketing, crop improvement, health benefits and many other things. The panel discussion is thus
planned to discuss following aspects
i) are millets potential answer for providing food and nutritional security for the growing
population,
ii) how advances in genomics can play important role in understanding millets better,
iii) how bioinformatics can help in identification of important genes in millets,
iv) what more need to be done to promote millets,
v) what does the future hold for millets in India?
vi) can celebrating IYM will change people’s perspective towards consuming millets?
Indian Conference on Bioinformatics 2023 - Inbix'23
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Model-Informed Drug Discovery and Development: From
Bioinformatics to Quantitative Systems Pharmacology (QSP)
Viji Chelliah, Certara UK
viji0312@gmail.com
Abstract
A large proportion of drug development projects fail in phase II and phase III clinical trials mainly
because of the lack of efficacy and unacceptable safety profile. One of the notable contributing
factors contributing to this failure is an inadequate understanding of the underlying disease
biology and target-disease linkage. This results in poor target choice, suboptimal target
modulation, unanticipated structure-based or mechanism-based toxicity, inappropriate patient-
population selection, and the absence of decision-making biomarkers. Therefore, finding novel,
druggable targets associated with high confidence in rationale for therapeutic efficacy and safety
remains a major challenge. Adoption of a discovery pipeline based on in-depth understanding of
disease biology and mechanisms is an absolute need for identifying potential targets for clinical
success. It has been reported that the implementation of the so-called 5R framework (right target,
right tissue, right safety, right patient, right commercial) increased the trial success rate from 4%
to 19%. Model-informed drug discovery and development involves the use of mathematical
models in exploring disease mechanisms, biomarker predictions, drug-dose predictions and
decision-making in pharmaceutical R&D contributing majorly to 5R framework. Quantitative
systems pharmacology (QSP) models, in particular, is now increasingly being employed to
translate the rapidly growing understanding and mapping of complex biology and
pathophysiology into a solid foundation for the efficient and rational development of novel
modalities, combination therapy and innovative treatment regimens to treat diseases in high
medical need areas. However, developing a QSP model is an extremely laborious process which
limits exploring multiple targets and mechanisms. Here, I will discuss about how bioinformatics
approaches can inform the development of QSP models to enable safe and effective new
therapeutics to advance more efficiently through the different stages of drug discovery and
development pipeline.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Journey Of Developing Reversible Inhibition of Sperm Under Guidance
(Risug®) As an Injectable Male Contraceptive with Special Reference
to Seminal Proteomics
A.S. Ansari (Centre for Advanced Studies, Department of Zoology, University of Rajasthan,
Jaipur), Barkha Khilwani (Centre for Advanced Studies, Department of Zoology, University of
Rajasthan, Jaipur) and N. K. Lohiya (Centre for Advanced Studies, Department of Zoology,
University of Rajasthan, Jaipur)
ansari3808@gmail.com
Abstract
Reversible inhibition of sperm under guidance (RISUG®) is an intravasal injectable male
contraceptive intend to be developed as an alternative to vasectomy to provide safe and long-term
contraception. It is a polymeric gel consisting of styrene maleic anhydride (SMA) dissolved in
dimethyl sulfoxide (DMSO) in 1:2 ratio when injected into the vas deferens blocks sperm passage
that traverse through and destroys them thereby rendering the subjects sterile. The non-invasive
and invasive reversal techniques, successfully demonstrated in langur monkeys, and rats and
rabbits, respectively. Extensive toxicological investigations carried out in different animal
species demonstrated safety of the procedure. Based on phase I, II, extended phase II and limited
phase III clinical trials data and on genotoxicity, mutagenicity and carcinogenicity study data, the
Indian Council of Medical Research, New Delhi conducted the phase III clinical trials at five
centers. The phase III clinical trial on RISUG injected subjects at our center indicated early onset
of contraception. The sperm functional tests showed a drastic reduction and marked sperm
deformities prior to azoospermia. The neutral α-Glucosidase, GPC and L-Carnitine concentration
was gradually and markedly reduced. No marked alteration was found in circulatory levels of
hormones, PSA and anti-sperm antibodies. Two-dimensional gel electrophoresis analysis
revealed a total of 235 protein spots in RISUG injected human subjects. When they compared
with fertile subjects 110 protein spots were matched. Out of which 57 spots were down-regulated
and 53 spots were up-regulated. Following MALDI TOF – TOF MS analyses Prolyl
endopeptidase-like (Fragment) (PREPL), Focal adhesion kinase 1 (Fragment) (PTK2) and two
different spots of Prolactin-inducible protein (PIP) were identified through MASCOT protein
database with NCBI blast search.
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It is concluded that RISUG, a safe and quicker contraceptive method, hopefully available for
mass application for human soon. The identified differentially expressed proteins following
RISUG® administration may be useful for management of infertility.
Indian Conference on Bioinformatics 2023 - Inbix'23
19
Candida Sterol 14α-demethylase – Patterns of Amino Acid
Substitutions and Azole Resistance
R. Shyama Prasad Rao (Center for Bioinformatics, NITTE deemed to be University, Mangaluru
575018)
drrsprao@gmail.com
Abstract
Drug resistance in bacteria and fungi is a major concern, and the azole-resistant Candida
infections are on the rise. One key mechanism is the emergence of resistant amino acid
substitutions in sterol 14α-demethylase, the target of azole drugs. While many such substitutions
are reported, it is unclear how prevalent they are or how they exert differential effects on different
azoles. We performed sequence analyses, molecular dynamics simulations, and free energy
calculations to understand the nature of substitutions and how they alter the binding free energy
and interactions of azoles with the protein. Based on a set of 2,222 instances, Y132F/H, K143R,
D116E, G464S were some of the frequent azole-resistant substitutions. While substitutions were
found at 133 residue positions, only a third of the azole-binding sites had any known substitutions.
The ligand-binding free energy for fluconazole, a short-tailed azole was far higher (-13.81
kcal/mol) than for VT1, a medium/long-tailed azole (-35.04 kcal/mol). There were differences in
the ligand-binding free energies after substitutions compared to the wild type protein. Multiple
substitutions also showed incremental differences in ligand-binding free energies. Concomitant
alterations in the residue orientations, and the distances between the residues and the ligand were
also observed. The results provide valuable insights into azole resistance and antifungal drug
discovery and optimization.
Indian Conference on Bioinformatics 2023 - Inbix'23
20
Zebrafish as a Powerful Model System to Investigate Cellular and
Developmental Mechanisms
Anil Kumar Challa (Shiv Nadar Institution of Eminence (Deemed to be University), Delhi NCR)
anil.challa@snu.edu.in
Abstract
The zebrafish, once a lesser-known tropical fish mentioned in Western scientific literature as
early as the 1820s, has emerged as a powerhouse in modern biomedical investigations. Initially
discovered for its striking appearance, it wasn't until the 1930s that researchers recognized its
potential as a valuable experimental system. However, it wasn't until the 1970s that the zebrafish
truly captured the spotlight, being adopted as a model organism for the study of neural
development and genetics. The zebrafish's rapid external development, transparency of embryos,
and genetic tractability have since propelled it to the forefront of scientific research. Its utility in
large-scale genetic screens and its biological similarities to humans make it an invaluable tool for
unraveling mysteries in cellular and developmental biology, contributing significantly to
advances in biomedical research. The talk will focus on a few success stories where zebrafish, as
a model system, played an important role in advancing our knowledge of cellular and
developmental biology, with far-reaching implications for both basic science and clinical
applications.
Indian Conference on Bioinformatics 2023 - Inbix'23
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New Drug Development - Lab to Launch (An Industry Perspective)
Manivannan Boomi (Chief Brand Promotion & VP - Plant Operation, Genaxy Scientific
Pvt Ltd., New Delhi/Solan (HP)
bmani@genaxy.com
Abstract
Discovery of new drugs for human application involves many years, many processes, often
failures with much uncertainty. There are many hits and trials to identify a new molecule from
basic research in to discovery and development of new drug in application point of view. The
process is too complex, time consuming, expensive and to resolve many operational issues to
target the human application. Developing a new medicine in regulatory point of view, i.e., an
Investigational New Drug (IND) primarily of its mode of action different from the approved
medicine intended for an indication that is not addressed yet, involves several stages, viz., target
identification, mode of action, process development, process standardization and validation,
proof of potency, safety and convenience to use in mass application. Development of a new
molecule towards commercial application that is from target identification to market
authorization approximately takes over 12 years many times, even more, costing about $ 1-2.5
billion and even more, an estimate based on analysis across several therapeutic development. The
developmental activities of an IND involve basic research, preclinical studies in animal models,
clinical studies in human participants (Phase I, Phase II and Phase III). The developmental phases
of new drug development in an Industry Perspectives from the developmental stage to Launch of
the product shall be discussed.
Indian Conference on Bioinformatics 2023 - Inbix'23
22
Bioinformatics Applications for Solving the Mystery of Complex
Human Diseases: An Aid in Healthcare
Tiratharaj Singh (CEHTI, Department of Biotechnology and Bioinformatics, JUIT)
tiratharaj@gmail.com
Abstract
Alzheimer’s Disease (AD) and various kind of cancer, both are very complex and multifactorial
diseases and a serious global burden to our society, where multiple enzymes simultaneously
activate due to the combination of genomics, interactome, and environmental factors. Due to the
complex nature of these diseases, combination of bioinformatics, genomics, systems biology and
molecular evolution can play a central role to identify the potential targets as well as the disease
mechanisms. We have used computational genomics and the systems biology approaches to find
the disease progression mechanisms. Quantitative systems biology approach was applied to
decipher the drug inhibition as well as disease progression mechanisms. A virtual AD cell model
was reconstructed using the systems biology graphical notation and then the concentration of
targets (key enzymes) and drugs were increased to observe the effects. We have predicted eight
key targets which can play a key role in AD progression. In another work we have created an AD
network and then heuristic approaches were used to find the novel information. We have
calculated several statistical parameters such as shortest path length, node betweenness, degree,
clustering coefficient and predicted the important nodes (hub nodes) which can act as a target for
AD pathogenesis. On the other hand, we have performed combination of these studies on various
human malignancies such as colorectal cancer, endometrial cancer and prostate cancer. Structure
based small molecular studies were also done for AD as well as cancer. From all the results we
have concluded that bioinformatics, computational genomics, structure-based drug design and
systems biology approaches are useful to analyze cancer and AD-like complex diseases data to
provide a meaningful piece of information to the experimental scientists for lab-based validations.
It is anticipated that this kind of analyses will provide a systematic protocol for the analysis of
complex diseases data and will help the biomedical community and healthcare sector for further
progressing in the positive direction.
Indian Conference on Bioinformatics 2023 - Inbix'23
23
Bioprospecting of Halotolerant Microorganisms by Metabolomic
Approach
Gurunathan Jayaraman (School of Bio Sciences and Technology, Vellore Institute of
Technology, Vellore)
gjayaraman@vit.ac.in
Abstract
Microorganisms can be found residing in nearly any type of environment, even in the harshest
chemical and physical conditions. These microbes have evolved through time to become adapted
to these "unfriendly" conditions. Due to this natural phenomenon, they are a valuable target for
the discovery of novel macromolecules as well as small compounds, which may have appealing
uses in numerous industrial processes or in therapeutics. Over the past few years, we have been
working to understand the adaptive traits of the microorganisms found in various salt pans along
India's coastal region, as well as their ability to produce proteins and metabolites of significant
industrial value. These microbes produce thermostable, halotolerant hydrolases with distinct
substrate specificity and are tolerant to heavy metal ions. Some of these organisms can survive
even at 3–5 M NaCl owing to alterations in their general morphology and enhanced
exopolysaccharide synthesis. Comparative metabolomics approach, using nuclear magnetic
resonance spectroscopy as well as mass spectrometry, has helped us to identify the changes in
the biochemical pathways which occur under salt stress and to discover new and rare small
molecules which are of industrial / therapeutic importance.
Indian Conference on Bioinformatics 2023 - Inbix'23
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In Silico Secretome Prediction and Expression Analysis of Potential
Effector Candidates of Fall Armyworm (
Spodoptera frugiperda
)
Sundaram Shilpi (Central University of Rajasthan), Vivek Verma (Central University of
Rajasthan) and Jayendra Shukla (Central University of Rajasthan)
jayendrashukla@curaj.ac.in
Abstract
Effector proteins, one of the major insect salivary gland components, alter host defense
mechanism(s) and facilitate pests for successful infestation of host plant. Fall armyworm,
Spodoptera frugiperda is a polyphagous lepidopteran insect infesting a wide range of agricultural
crops. Despite being one of the world’s deadliest pests, no information about the effector proteins
of S. frugiperda is available, till date. In view of the secretory nature of effectors, an in silico
secretome of S. frugiperda was generated. For this, we performed an in-silico analysis of
interproscan-annotated protein sequences of S. frugiperda (derived from its transcriptome) using
established secretome prediction pipelines. Out of 21,779 protein sequences of S. frugiperda, 821
proteins were predicted to be secretory in nature, leading to the generation of an in silico
secretome database of S. frugiperda. The proteins of S. frugiperda secretome were categorized
into different functional groups as per their annotated functions. The expression of 40 selected
candidates was analyzed in different tissues (head, gut, salivary gland and fat body) of S.
frugiperda, which revealed 14 candidates to be exclusive to a single tissue. In addition, expression
of 13 candidates were found to be exclusive to gut or salivary glands or to both the tissues
indicating that they may be secreted out from the insect's body and serve as potential effector
proteins. Further, the expression (in the gut and salivary gland of S. frugiperda) of potential
effector candidates will be compared between the insects fed on artificial diet versus the insects
fed on plants which will help in the identification of effector proteins of S. frugiperda.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Biogenic Iron Oxide Nanoparticles and CRISPR as a Panacea for
Combatting Global Warming
K Sangeetha (School of Biotechnology, Amrita Vishwa Vidhyapeetham), S Induja (School of
Biotechnology, Amrita Vishwa Vidhyapeetham), S Ajeesha (School of Biotechnology, Amrita
Vishwa Vidhyapeetham), M.K. Reddy (nternational Centre for Genetic Engineering and
Biotechnology, New Delhi, India), V. Mohan M. Achary (nternational Centre for Genetic
Engineering and Biotechnology, New Delhi, India), Nair Bipin (School of Biotechnology,
Amrita Vishwa Vidhyapeetham), Panicker Manoj (TKM College of Arts and Science, Kollam)
and Vishnudasan Dalia (School of Biotechnology, Amrita Vishwa Vidhyapeetham).
daliavishnudasan@gmail.com
Abstract
Biogenic iron oxide nanoparticles (FeNp) are synthesized using green chemistry approaches and
are hence very eco-friendly and sustainable. These FeNp have numerous applications, particularly
in the field of agriculture and wastewater remediation. FeNp is used as a fertilizer to improve the
growth and yield of crops. FeNp is known to activate the oxidation defense system, scavenging
reactive oxygen species (ROS) and adsorbing heavy metals. Studies in our lab indicate the
potential of FeNp in enabling the plants to overcome abiotic stresses such as - Drought stress,
Salinity Stress, and Arsenic stress. These nanoparticle-based iron fertilizers have enormous
potential in agriculture because of their low cost and low toxicity.
Wastewater remediation using Biogenic iron nanoparticles (FeNp) encompasses the removal of
organic pollutants, heavy metals, microplastics, and the catalytic degradation of pollutants.
Biogenic iron oxide nanoparticles (FeNp) can be used to adsorb contaminants from wastewater
and decolorize dyes in wastewater. Studies in our lab indicate using FeNp for wastewater
remediation and microplastic removal. Magnetic iron oxide (FeNp - Fe3O4) nanoparticles were
effective in removing microplastics, including polyethylene terephthalate, high-density
polyethylene, LDPE, polyvinyl chloride and polypropylene. Hence FeNp has potential
applications in wastewater treatment. Furthermore, we addressed the safety issue of the FeNp that
would remain in the aquatic environment. The aquatic plant Azolla was exposed to microplastics,
and its amelioration was initiated with FeNp. Interestingly, Azolla and its symbiont are negatively
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impacted by microplastics (Mp), but use of FeNp enables the Azolla plant to overcome Mp
pollution.
Rice (Oryza sativa) is a dietary staple for half the world’s population. To meet the growing demand
for rice and adaptability to changing environmental conditions, the CRISPR technology is being
employed to alter its traits. Phospholipase D beta 1 (PLDβ1) a key enzyme in lipid metabolism
was altered using CRISPR Cas9. By manipulating the PLDβ1 gene, adaptability to adverse
conditions, such as drought and salinity was noticeably achieved in Samba Masuri BPT-5402.
Moreover, PLDβ1 is responsible for the modulation of other PLD isoforms expression in rice.
Knock out of PLDβ1 gene elevates expression of stress-inducible genes, osmotic biosynthesis
genes, lignin biosynthesis genes, and enhances ROS scavenging activity, which together contribute
to improved abiotic stress tolerance. This study provides novel insights into the function of the
PLDβ1 gene in rice. Further analysis of their regulation under abiotic stress will promote the
molecular breeding of stress-tolerant rice.
Indian Conference on Bioinformatics 2023 - Inbix'23
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A Computational Study of Conformational Transitions in Intrinsically
Disordered Regions on Complexation
Prachi Bhargava1, Madhabendra Mohon Kar1, Paramveer Yadav1 and Amita Barik1*
Department of Biotechnology, National Institute of Technology, Durgapur, India
amita.barik@bt.nitdgp.ac.in*
Abstract
The structure–function paradigm has been considered as the central dogma of structural biology.
In contrast to it, a different group of proteins has been given recognition that do not adopt their
defined three-dimensional structure but plays important role in different biological functions. This
group of proteins is referred as Intrinsically Unstructured or Intrinsically Disordered Proteins
(IDPs) and the regions are termed as Intrinsically Disordered Regions (IDRs). IDRs play a pivotal
role in modulating cellular processes and signaling pathways and can provide valuable insights
into drug design as their structural disorder can aid in ligand selection for drug development. IDRs
often act as hubs in protein-protein interaction (PPI) networks due to their conformational
flexibility, allowing them to interact with multiple biomolecules. They can adopt a well-defined
tertiary structure upon binding, retain some degree of disorder, or remain completely disordered,
forming fuzzy complexes. We analyse the conformational changes in IDRs upon complex
formation using unbound proteins and the protein complexes. IDRs are enriched in polar charged
amino acids and are depleted in aromatic residues. A study of IDRs in unbound proteins which get
ordered upon complex formation after binding to suitable partners such as other proteins, DNA
and RNA was carried out. An analysis of secondary structures reveals that 79.78% residues in
unbound polypeptides form coils upon binding to suitable partners. We also observe that such
residues are located at the interface of protein complexes suggesting that they contribute to the
stability of the complexes. Amino acids that undergo transitions also contribute in hydrogen bond
formation in the protein complexes. Our findings provide fundamental insights into the underlying
principles of molecular recognitions by disordered regions. There are some structured regions in
the unbound proteins which upon complexation become disordered and it will be intriguing to
learn more about these ordered-to-disordered transitions upon complex formation.
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Keywords: Intrinsically Disordered Regions (IDRs), protein complexes, unbound proteins,
conformational changes, interface, hydrogen bonds
Indian Conference on Bioinformatics 2023 - Inbix'23
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Design and Validation of CRISPR-Cas13a-Based Tool for Detection of
K. pneumoniae
Gargi Bhattacharjee, Vijai Singh
Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, Gujarat,
India
vijaisingh15@gmail.com
Abstract
Every year millions of lives lost due late and inadequate diagnosis. A rapid, specific and sensitive
diagnosis play a vital role in infection control and public health initiatives to restrict disease
transmission in highly equipped medical centre. An ideal diagnostic procedure is one which is
rapid, affordable, error-free, and enables point-of-care (POC) operation without the necessity of
technical trained person, expensive instrumentation, or power supply. A test that endows these
features could aid in the early detection of highly virulent pathogens, isolation to prevent disease
spread, and facilitate prompt medical attention and timely cure. In an attempt of addressing the
unmet need for reasonably priced, logistically feasible and distributable detection devices for the
rapid detection of Klebsiella pneumoniae that can function without the assistance of sophisticated
laboratory equipment, a reliable CRISPR-assisted lateral flow assay-based detection platform was
devised. In this study, we established a rapid and sensitive CRISPR-Cas13 diagnostic assay using
a lateral flow-based platform by amalagamating recombinase polymerase amplification (RPA) for
sequence-amplification and the Cas13a orthologue from Leptotrichia wadei (Lwa)Cas13a for
detection of hypervirulent phenotypes of K. pneumoniae. We employed CRISPR detection
platform with RPA to first demonstrate the detection of Klebsiella spp. by aiding species-specific
detection of K. pneumoniae by targeting housekeeping rpoB gene, and then proceeded to detect
the hypervirulent strains of K. pneumoniae. The capsular polysaccharide regulating gene rmpA
was opted as the target. Out of 18 K. pneumoniae strains, the devised tool detected K. pneumoniae
M59 and K. pneumoniae KP109 strains with the presence of rmpA. In the long-run, the idea is to
generate an instrument free platform for routine diagnosis of K. pneumoniae from serum, urine
and saliva samples from patients. This would enable the healthcare personnel to encourage proper
and timely treatment of infections caused by K. pneumoniae.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Keywords: Klebsiella pneumoniae, CRISPR-Cas13a, Hypervirulent, Diagnostics, Multidrug
resistance
Indian Conference on Bioinformatics 2023 - Inbix'23
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Oral Presentation
MicroRNAs and Gene Expression Analysis for their Regulatory Role in
Alzheimer’s Disease
Vinamrata Sharma 1, Saumya Tyagi 1, Vikram Singh2 and Tiratha Raj Singh3*
1Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology
(JUIT), Solan, India
2Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh
(CUHP)
3Centre of Excellence in Healthcare Technologies and Informatics (CHETI), Department of
Biotechnology and Bioinformatics, Jaypee University of Information Technology (JUIT), Solan,
India.
tiratharaj.singh@juitsolan.in
Abstract
Rationale/Motivation: To identify the key genes and miRNAs that are connected to molecular
events in Alzheimer's disease (AD) and to examine how they interact and regulate these biological
entities. AD is a progressive neurodegenerative disease that is characterized by changes in
neuropathology and a decline in cognitive function. There is no cure for AD, and current treatments
are only able to slow the progression of the disease. miRNAs are small, non-coding RNAs that
play an important role in gene regulation. They can bind to mRNAs and either promote or inhibit
their translation into proteins. miRNAs have been shown to be involved in a variety of diseases,
including AD. This extensive study about miRNAs can be utilized in the future to familiarize with
the basic mechanisms underlying the over- and under-expression of genes in AD. As a result, by
looking and evaluating the miRNAs targets in the future molecular research we can tune their
therapeutic and regulatory role. We hope to identify the key genes and miRNAs that are involved
in AD so that they can develop new diagnostic and therapeutic strategies for the disease. By
understanding how miRNAs regulate gene expression in AD, they may be able to develop new
drugs that can target these miRNAs and alter the course of the disease.
Objective: To evaluate which target genes and microRNAs are implicated during Alzheimer's
disease (AD), as well as which of them are often overexpressed and under expressed in AD, and
to comprehend the regulatory network of these genes. Using a range of datasets, this study
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performed a bioinformatic analysis on miRNA-AD studies in order to find 250 expressed genes
and 67 expressed miRNAs. The study then looked at the regulatory networks and pathways
associated with the genes that govern AD. The TAU and APOE genes were found to have a major
effect in AD. The goal of the study is to discover miRNAs and the target genes that are both
frequently overexpressed or under expressed in AD, as well as to comprehend the regulatory
network behind these miRNAs. This information can be used in future research to develop new
therapeutic and regulatory strategies for AD.
Materials and Methods: In this study, we conducted a bioinformatic analysis of miRNA-AD
studies using a variety of databases, including GEO Database, STRING, miRBase, KEGG,
TargetScan, and Database for Annotation, Visualization to identify the miRNAs that are frequently
overexpressed and under expressed in AD. We also analyzed the genes and miRNA expression
profiles associated with AD, for which we identified the miRNA target genes.
Data collection: We collected data from the GEO Database, STRING Database, miRBase, KEGG,
TargetScan. Identification of differentially expressed miRNAs: We used miRBase to identify the
target genes of the differentially expressed miRNAs. Identification of miRNA target genes: We
used the GEO2R tool to identify differentially expressed miRNAs in AD patients compared to
healthy controls. Pathway analysis and Gene ontology analysis: We used the KEGG and STRING
Database to analyze the pathways of the genes that control AD.
Results and Discussion: We conducted a bioinformatic analysis of miRNA-AD studies to identify
miRNAs that are frequently overexpressed and under expressed in Alzheimer's disease (AD). We
found a total of 250 expressed genes and 67 expressed miRNAs associated with AD. The GEO2R
software provides the logFC values for each gene and miRNA, which measure the fold change in
expression between the two age groups. The logFC (log fold change) value in GEO2R is a measure
of the fold change in expression of a gene or miRNA between two groups of samples. It is
calculated by taking the log base 2 of the ratio of the average expression values in the two groups.
The logFC value is a useful metric for identifying differentially expressed genes (DEGs) in
GEO2R. A positive logFC value indicates that the gene or miRNA is upregulated in the older age
group, while a negative logFC value indicates that it is downregulated. After gathering this dataset
from the GEO Dataset and utilizing GEO2R to analyze it, we identified some common genes that
are either overexpressed or under expressed together with the miRNAs that are linked to them.
This suggests that these common genes may be primary in AD. It concludes that this extensive
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study about miRNAs can be utilized in the future to familiarize with the basic mechanisms
underlying the over- and under-expression of genes in AD. They suggest that by looking and
evaluating the miRNA targets in the future molecular research, we can tune their therapeutic and
regulatory role.
Conclusions: This study provides valuable insights into the molecular mechanisms underlying
AD. The identification of common genes that are either overexpressed or under expressed together
with the miRNAs that are linked to them is particularly noteworthy. This suggests that these
common genes may be key targets for future therapeutic development. The study also highlights
the importance of miRNA research in AD. miRNAs are small molecules that play a critical role in
gene regulation. By understanding how miRNAs regulate gene expression in AD, we can develop
new and more effective treatments for this disease. Overall, this study is a significant contribution
to the field of AD research. The findings could have a major impact on the development of new
diagnostic and therapeutic tools for this devastating disease.
Keywords: Alzheimer’s Disease, microRNAs, Pathways, Bioinformatics Tools, Expression
studies
Indian Conference on Bioinformatics 2023 - Inbix'23
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Ancient Antimicrobial Resistance Genes Unearthed: Insights from
Pleistocene Permafrost and Ice Core Metagenomes
Sankaranarayanan Gomathinayagam, Kodiveri Muthukaliannan Gothandam*
School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore.
gothandam@gmail.com*
Abstract
Motivation: The rationale for this study is rooted in the ancient origins of antibiotic resistance,
predating the clinical use of antibiotics. Previous reports have identified antimicrobial resistance
genes in Pleistocene permafrost sediment and ice cores, primarily through PCR amplification of
targeted loci associated with potential antimicrobial resistance genes. However, in the broader
natural context and considering the period when antibiotics were not extensively used, there may
exist numerous unidentified antimicrobial resistance genes. Therefore, this study aims to unveil
the presence of such genes in metagenome data extracted from ice cores and permafrost samples.
Objectives: To identify and retrieve nucleotide sequences of putative antimicrobial resistance
genes from early Pleistocene to late Pleistocene permafrost and ice-core metagenome reads. To
computationally model the protein structure of the retrieved ARGs and temporally analyze the
evolution of identified putative ARGs.
Materials and Methods: A total of 2225.4 Giga base pairs of data were obtained from publicly
available sequence read archives and repositories, encompassing samples aged from 19,000 years
to as old as 1,100,000 years. Simultaneously, a redundant database of antibiotic resistance genes
was created, combining several publicly available databases. The following databases were
employed: Comprehensive Antibiotic Resistance Database (CARD) ARG database (v3.0.9)
MarillynR Tetracycline Database (http://faculty.washington.edu/marilynr/, accessed in November
2022), MEGARes database (v3), NCBI Refgene Catalogue for Antibiotic Resistance Genes
(v2020.10.12), ResFinder (v4.1.0), Beta-Lactamase Database (http://bldb.eu, accessed in
November 2022), NDARO (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA313047,
Indian Conference on Bioinformatics 2023 - Inbix'23
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accessed in January 2023), CBMAR
(http://proteininformatics.org/mkumar/lactamasedb/index.html, accessed in November 2022),
MUSTARD (http://mgps.eu/Mustard/, accessed in November 2022), and ResFinder FG 2.0.
Subsequently, the metagenome reads were mapped to the newly created redundant ARG database
using a k-mer alignment (KMA) tool. The reads that mapped to the genes in the database, meeting
specific threshold criteria, were subjected to scrutiny, and protein models were constructed.
Results and Conclusions: Antimicrobial resistance is indeed of ancient origin. The study focused
on identifying antibiotic resistance genes within the 'antibiotic inactivation' class, revealing the
presence of vancomycin resistance genes, beta-lactamase genes, tetracycline resistance genes, and
notably, tigecycline resistance genes. It was observed that the abundance of antimicrobial
resistance genes (ARGs) was influenced by the depth of metagenomic sequencing and not
correlated with the age of the retrieved samples. Additionally, protein modelling demonstrated
significant similarities to contemporary ARGs. However, the phenotypic activity of the identified
ancient ARGs remains untested.
Keywords: Ancient metagenome, Permafrost, Ice-core, Antimicrobial resistance genes,
Pleistocene
Indian Conference on Bioinformatics 2023 - Inbix'23
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Exploring Antimicrobial Compounds in
Streptomyces
Strain VITGV100
(MCC 4961) with Chemical Elicitor
Madhuri Mukindrao Moon and John Godwin Christopher*
1School of BioSciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu,
India
godwinj@vit.ac.in *
Abstract
Streptomyces strains have long been recognized as prolific producers of bioactive compounds,
including antibiotics. Streptomyces strain VITGV100 is exhibiting promising antimicrobial
compounds. Genome analysis of Streptomyces strain VITGV100 reveals thirty-five cryptic
biosynthetic gene clusters that remain dormant under standard laboratory conditions which
includes peptide, polyketide’s, terpenes, siderophores indole and melanin biosynthesis. This study
explores the activation of these cryptic biosynthetic genes within Streptomyces strain VITGV100,
using nutrient broth with 0.5% dimethyl sulfoxide as chemical elicitor at different incubation
periods such as 7, 14 and 21 days of culture. Through a systematic screening process, we identified
the 7th day of crude extract exhibited significant antimicrobial activity, against selected human
pathogens Bacillus subtilis (MTCC 2756), Staphylococcus aureus (MTCC 737), Escherichia coli
(MTCC 1687) and Pseudomonas aeruginosa (MTCC 3541). Among these pathogens maximum
zone of inhibition of 29 mm at 100 μg/ml was recorded against Staphylococcus aureus and
minimum inhibition zone of 20 mm at 25 μg/ml against Pseudomonas aeruginosa were recorded.
The extract was analyzed in GC-MS for a detailed chemical characterization. The results revealed
that there were 45 distinct peaks from each extract on different days. Only 30 peaks recorded for
their controls. The GC–MS data showed the presence of unique compounds such as
cyclopentathiazole, thymol lacthydrazide, tyrosol, acetate, trimethylsilyl derivative and few more
compounds responsible for antimicrobial activity. Thus, the results of the present study reveal
Streptomyces strain VITGV100 is an excellent organism for synthesizing antibacterial compounds.
Keywords: Streptomyces, secondary metabolites, elicitor, GCMS analysis, dimethyl sulfoxide,
antimicrobial compounds.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Network Pharmacology Based Study on the Mechanism of Aloe Vera for
Treating Psoriasis
Vignesh B, Aadil Ahmed Irshath, Thomas Theodore, Anand Prem Rajan*
Vellore Institute of Technology
janandpremrajan@vit.ac.in*
Abstract
This study aims to analyze the targets of the effective active ingredients of Aloe vera in psoriasis
by network pharmacology and molecular docking and to explore the associated therapeutic
mechanism. Psoriasis is a condition in which skin cells build up and from scales and itchy, dry
patches. There is no cure for psoriasis, but treatments can help to reduce the effect of psoriasis.
There are more than 8 million cases per year. In that way there is lot of research in treatment of
psoriasis using natural plants. Nature has a source of medicinal plants. Plants such Aloe vera as
have adverse effects against psoriasis. The phytochemical constituents of the plants include
alkaloids, flavonoids, saponin, phenol, glycosides and tannins. The effective active ingredient of
Aloe vera was determined from the TCMSP database and the drug ingredient target network was
constructed using the cystoscope software. String database was used to analyse the PPI. Then GO
and KEGG analysis was done by the bioinformatics tool and the top 20 key signalling pathways
were obtained. The drug ingredient target key pathway was determined using cystoscape software.
Finally docking was performed to finalize the binding efficiency of the active ingredient and the
target. The results of the study show the active ingredients against the psoriasis disease.
Keywords: Aloe Vera, Psoriasis, TCMSP, Protein-Protein Interaction, Cystoscope, Docking
Indian Conference on Bioinformatics 2023 - Inbix'23
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Targeting Mitochondrial Dynamics: An In-silico Approach for
Repurposing Antifungal Drugs in OSCC Treatment
Neha Sivakumar1, Rohith Raali1, Harsh Vardhan J1, and Suresh P.K2.
1Department of Biotechnology, 2Department of Biomedical Sciences,
School of Bio Sciences and Technology, Vellore Institute of Technology
p.k.suresh@vit.ac.in *
Abstract
Rationale: Drug repurposing for cancer treatment is a valuable strategy to quickly identify existing
drugs with known safety profiles that could effectively restrain tumorigenesis and by potentially
reducing preliminary screening costs. One in ten cancer patients within the Indian subcontinent
suffer from OSCC, primarily due to incessant chewing of betel plant derivatives. Among the
different therapeutic modalities, concomitant administration of the chemotherapeutic agent
(cisplatin/paclitaxel) is the treatment of choice. Multiple studies have also pointed out to the
inefficiency of these chemotherapeutic agents to stun the growth of the neoplasm. Analysis of the
oral mycobiome of OSCC patients has projected the intrinsic role of Candida albicans in
potentiating OSCC. In the context of treating OSCC, repurposing antifungal drugs emerges as a
promising approach, as these drugs could target both the cancer cells and the fungal infections.
Cancer cells often have heightened energy requirements, and targeting mitochondrial proteins to
disrupt mitochondrial division and induce dysfunction which can lead to cell death, offering a
potential method for treating OSCC and improving clinical outcomes for affected individuals. It’s
also imperative to note that in most cases tumour relapse is mainly owed to mitophagic flux
associated with the presence of cancer stem cells (CSC) within the hypoxic niche of the solid
neoplasia. Mitochondrial quality control i.e., Biogenesis, fission, fusion and mitophagy are
essential to maintain a healthy mitochondrial population. Thereby, deregulation of this essential
homeostasis by targeting mitophagy and promoting apoptosis points to Anti CSC therapeutic
capabilities.
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Objective: The objective of this study is to investigate the potential of antifungal ligands in
targeting mitochondrial proteins for the treatment of OSCC. To achieve this, a comprehensive
research methodology has been employed, involving docking studies, ligand profiling, molecular
dynamics simulations, and with a focus on identifying the most promising candidates for the
development of a novel nano-formulation.
Materials and Methods:
A thorough literature search identified 18 mitochondrial proteins associated with mitochondrial
dynamics. When experimental structures were available, 3D X-ray crystallography data from the
RCSB Protein Data Bank was gathered. In cases where experimental structures were absent,
computationally annotated structures from the AlphaFold Protein Structure Database and
molecular models from the SWISS-MODEL repository were used. A dataset of 125 unique
antifungal ligands was collected from sources like PubChem and the zinc database. Ligand
pharmacokinetics and toxicity properties were assessed using SwissADME, ProTox-II, and
Prediction of activity spectra for substances tool (PASS). Computational Atlas of Surface
Topography of proteins (CASTp) was utilized to identify binding pockets in target proteins. These
pocket values were used to create grid boxes for AutoDock Vina and AutoDock 4, estimating the
most likely binding locations. Protein structures obtained from RCSB PDB were prepared for
molecular docking using CHIMERA and PyMOL to ensure the absence of ligands and additional
chains. AutoDock Vina was initially employed to assess the binding affinities of selected ligands
with the target proteins. A reference chemotherapeutic drug, paclitaxel, was included for
comparison, as it is known for its anti-mitochondrial properties. Ligand-protein combinations with
binding affinities lower than that of paclitaxel were eliminated, ensuring the inclusion of only high-
affinity interactions. The best ligand for each protein was selected, and AutoDock 4 was used to
calculate Estimated Inhibition Constant (Ki) values. A lower Ki value indicates a higher binding
affinity to the protein, and Ki values within the picomolar range or below were considered
significant. For the ligand-protein combinations with Ki values in the picomolar range or below,
GROMACS (2023.2) was employed to conduct molecular dynamics simulations. These
simulations provide insights into the dynamic behavior of ligand-protein complexes, further
validating the binding affinities and interactions observed in the docking studies.
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Results: AutoDock Vina revealed promising binding affinities for various antifungal ligands
compared to the reference drug paclitaxel. Notable binding affinity values were obtained for a
range of mitochondrial proteins, with the top values being reported for MID51, MID49, VDAC,
and others. Molecular docking studies with AutoDock 4 identified five ligand-protein
combinations with Ki values in the picomolar range or below, indicating high binding affinity. The
proteins MID51, MID49, VDAC, DRP1, and PDK1 exhibited Ki values ranging from 63.60 pM
to 715.31 fM. The free energy of binding for these interactions was also assessed, reinforcing their
significance. ADME prediction using Swiss-ADME favored itraconazole as a promising drug
candidate due to its favorable properties. Certain parameters like molecular weight in the context
of Lipinski’s rule were not considered highly significant in the context of developing a liposome-
encapsulated drug delivery system. The PASS analysis highlighted the antifungal properties of
both natamycin and itraconazole. Natamycin exhibited anti-neoplastic and immune stimulant
properties, while itraconazole was predicted to have multiple effects, including Cytochrome P450
inhibition and Lanosterol 14 alpha demethylase inhibition with a probability margin over 0.7.
Predicted LD50 values for antifungal drugs natamycin and itraconazole were reported, indicating
their safety profiles. Both drugs were predicted to be inactive against the tumor-suppressing
protein p53 and were not considered carcinogenic or mutagenic. Itraconazole was predicted to be
hepatotoxic, although this effect could be managed through dosage control. Natamycin was not
predicted to be hepatotoxic, while and both drugs were forecasted to cause immunotoxicity. In
order to refine our screening and selection strategy based on the stability and temporal nature of
the binding of the ligand to the receptor, MDS based experiments are underway.
Conclusion: In this study, we analysed protein ligand interactions through docking simulations
and comprehensive ligand profiling was performed using SwissADME, ProTox-II and PASS.
Highest docking score was conferred by MID51 and itraconazole with binding affinity of -16.57
Kcal/mol and a low KI value of 715.31 fM. Taken together with the positive ligand profiling of
itraconazole, we believe this potent drug to target the Achilles heel in dysregulating mitochondrial
biogenesis and promoting apoptosis of the neoplastic cells. Further validation of these results is
warranted for through in-vivo analysis.
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Keywords: Oral squamous cell carcinoma, Mitochondrial dynamics, Antifungal drugs, In-
silico, Drug repurposing, Molecular docking
Indian Conference on Bioinformatics 2023 - Inbix'23
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Stratifying Breast Cancer Subtypes Using DNA Methylation Markers
Sri Lakshmi Bhavani Pagolu1*, Suba S2*, Nita Parekh2
1IIIT Hyderabad, 2International Institute of Information Technology
nita@iiit.ac.in *
Abstract
DNA methylation aberrations are common in cancers and are known to affect. Understanding how
these affect the transcriptome can provide insights into subtype specific variations and treatment
outcomes. In this study we carried out multi-omics profiling (DNA methylation and gene
expression) in breast cancer patients from TCGA-BRCA dataset and propose a novel set of 35
methylation-based prognostic markers for subtype-specific disease stratification. Gene-set
enrichment and pathway analysis of the predicted markers using MSigDB and DAVID revealed
their role in mammary gland development pathway, various signaling pathways (ERBB2,
NOTCH, etc.), and other cancer pathways, and showed clear association with genes affected by
hormone receptor status. We show that the reported DNA methylation signature has high
discriminative power in classifying breast cancer samples into three molecular subtypes, viz.,
Luminal, HER2-enriched and Triple Negative, by using six machine learning approaches. An
accuracy of 94.12% and MCC of 0.87 is obtained in stratified 5-fold cross-validation for the three-
class classification using SVM-RBF.
Keywords: Breast Cancer, Subtype classification, Machine learning, DNA methylation
Indian Conference on Bioinformatics 2023 - Inbix'23
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Renal Sensing of Gut Microbiota Derived- Metabolites in Diabetic
Chronic Kidney Disease: An Integrated Approach Using Network
Pharmacology and Molecular Docking
G.R. Saranya1, Pragasam Viswanathan*, Renal Research lab, Pearl Research Park, School of
Biosciences and Technology, Vellore Institute of Technology-632014
pragasam.v@vit.ac.in *
Abstract
Rationale: Metabolites from the gut microbiota serve as defining molecules in the gut-kidney
crosstalks. However, active renal sensing gut metabolites and its mechanism in the context of
diabetic chronic kidney disease is still unknown.
Objectives: This study employs the computational network pharmacology framework to examine
the primary metabolites and mechanistic action of gut microbiota against diabetic chronic kidney
disease (DCKD).
Materials and Methods: (1) For this study, the selection of hum
an gut microbial targets was retrieved from the gutMgene database, based on the SMILES format,
each metabolite was identified using the PubChem database. (2) Using DisGeNET, GeneCard,
NCBI and OMIM database Diabetes CKD targets were identified for Homo sapiens (3)
Computational analysis like protein-protein interaction (PPI) networks, gene ontology and Kyoto
encyclopedia of genes and genome pathway analysis, identification of metabolites for the key
target using molecular docking, evaluation of drug-likeness properties for the key targets were
done using ADMET lab.
Results and Discussion: A total of 205 gut metabolites were retrieved from the gutMgene
database, and 1304 targets were obtained from Swiss Target Prediction (STP) database, and 1470
targets from Similarity Ensemble Approach (SEA). We obtained 203 targets for DCKD and
identified 574 overlapping targets. Following the retrieval of 203 and 222 targets from the
gutMgene database, twenty-seven (27) targets were identified as final DCKD targets of
metabolites by microbiome. Based on enrichment analysis, host/microbiome protein-protein
interaction, gene-disease association results predicted NFK-b1, AKT1, EGFR, JUN and RELA via
MAPK signalling pathway would facilitate the progression of DCKD. The gut microbiota-
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metabolite-substrate-sample source (GMMSS) network analysis indicates that metabolites
originating from the gut microbiome significantly regulate NFkb1 and EGFR gene expression
under DCKD conditions through MAPK signalling pathway. Specific bacteria intestinal epithelia,
colonic region including Bacteroides distasonis, Bifidobacterium adolescentis, Faecalibacterium
prausnitzi A2-165, and Bacteroides vulgatus, build a community that inhibits renal NF-kb1 a target
protein expressed at higher glucose concentrations. These bacteria metabolize the unknown
substrate to produce Indole 3 propionic acid which were reported to improves the blood glucose
level, insulin sensitivity and maintains the intestinal barrier integrity in host.
Furthermore, we discovered that the genus Lachnospiraceae prevents the activation of EGFR, a
tyrosine membrane protein expressed after renal damage in CKD patients with diabetes mellitus.
When NF-Kb1 and EGFR target proteins were docked with Indole 3-propionic acid, a good
binding energy affinity was observed.
Conclusions: This work showed that the probiotic gut bacteria Faecalibacterium prausnitzi A2-
165 from Firmicutes phylum could enhance the production of the metabolite indole-3 propionic
acid. It is also known that these metabolites have renal sensing properties and that they could
potentially be used to treat chronic kidney disease in diabetic individual. In addition, it provides
thorough insights that could serve as the basis for further research and supports gut health.
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Prediction, Design, Molecular Docking and Dynamics Simulation of
Novel Antimicrobial Peptides from Aegle Marmelos Against
Staphylococcus aureus
Rudra Awdhesh Kumar Mishra, Kodiveri Muthukaliannan Gothandam* School of Bio-
Sciences and Technology, Vellore Institute of Technology, Vellore - 632014, Tamil Nadu,
India
gothandam@gmail.com *
Abstract
Rationale: Staphylococcus aureus is a nosocomial pathogen responsible for the cause of various
range of infectious diseases such as skin infections (acute and sometimes chronic), soft tissue
infections; and even life-threatening systemic disease. The emergence of antibiotic-resistant strains
such as methicillin-resistant Staphylococcus aureus and multidrug-resistant S. aureus poses a
significant challenge to traditional antibiotic therapy. To overcome antibiotic resistance among
patients infected with S. aureus infections, there is an immediate need to find alternative
therapeutic agents. Several molecules have shown promising results in combating S. aureus
infections among resistant strains. However, antimicrobial peptides have emerged as a prominent
alternative for combating infections because of their desired characteristics and properties.
Antimicrobial peptides have garnered increasing attention as potential alternatives due to their
broad-spectrum antimicrobial activity and low propensity for developing resistance.
Objectives: The study's objective is to predict, and design a novel antimicrobial peptide from the
Aegle marmelos proteome sequences and validate its anti-MRSA property through molecular
docking & dynamics simulation against Staphylococcus aureus.
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Materials and Methods: Antimicrobial peptides were predicted from Aegle marmelos using in–
silico digestion using five different enzymes such as Chymotrypsin (low & high specificity),
trypsin and Pepsin (pH 1.3 and >2) through the EXPASY peptide cutter tool. Then, digested
shorter peptide sequences were analyzed for their antimicrobial property by the DBAASP server.
The physicochemical property of the predicted AMPs was analyzed using two different tool APD3
(Antimicrobial peptide Database 3) and PROTPARAM where characteristics such as half-life
time, instability index, charge, pI, hydrophobicity, and GRAVY value were analysed. Further,
ADMET properties were predicted for the predicted AMPs using the ADMETLAB 2.0 tool, before
peptide sequences were converted to SMILE characters for the prediction of ADMET property.
The structure of the AMPs was predicted using the PEPFOLD3.0 server where the best peptide
model was taken for the molecular docking study against the Staphylococcus aureus target protein
using the HADDOCK tool. Then molecular dynamics simulation was performed for the complex
having the maximum binding affinity for 50 ns using the Desmond package - 2018 version.
Results and Discussions: Different protein sequences were digested using different enzymes to
obtain linear shorter peptide sequences, which were further subjected to the prediction of AMP
property using the DBAASP server. From the server, we obtained 50 peptide sequences, for which
physicochemical properties were characterized using different servers and the non-toxic peptides,
and stable were further utilized for structure prediction and molecular docking studies. Molecular
docking studies for the peptide were performed against the two-target protein of S. aureus (PDB
ID – 2W9S and 7O4M). Protein-peptide docking was performed using the HADDOCK server, and
from the docking study, we have two peptides (PI - WGQPKSKITH and P II –
GKEAATKAIKEWGQPKSKITH) that were able to interact with the target protein of S. aureus
giving maximum binding affinity at the binding pocket of the protein. Protein-peptide interactions
were further validated using molecular dynamics simulations for 50 ns using the Desmond
package. Dynamics simulation was performed for peptide, protein alone and protein-peptide
complex and simulation results were analysed using the Simulation Interaction diagram tool from
the Desmond package. From the studies, it has been observed that these two peptide sequences can
be further used for in-vitro and in-vivo studies against drug-resistant S. aureus infections.
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Conclusions: In summary, the findings from this study highlight the potential of the identified
peptide sequences (PI and PII) to serve as valuable candidates for further in-vitro and in-vivo
studies targeted at combating drug-resistant S. aureus infections. These peptides are promising
therapeutic agents in the fight against antibiotic-resistant strains, offering new avenues for
addressing the growing challenges posed by S. aureus infections.
Keywords: Aegle marmelos, Antimicrobial peptides, Drug-resistant Staphylococcus aureus
Indian Conference on Bioinformatics 2023 - Inbix'23
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Structure Activity Relationship Studies of Anacardic Acid Derivatives:
Implications in Cancer Biology
Sandhya Padmakumar *, Geetha Kumar*, Bipin Nair* Amrita Vishwa Vidyapeetham, India
sandhyap@am.amrita.edu, gkumar@am.amrita.edu, bipin@am.amrita.edu
Abstract
SUMOylation is a post-translational modification pathway that is implicated in the manifestation
of various diseases such as cancer, viral infections, diabetes, and neurodegenerative disorders.
SUMOylation influences different hallmarks of cancer, such as cell senescence, carcinogenesis,
cell differentiation, and apoptosis. Therefore, an imbalance in SUMOylation could affect
metastasis, angiogenesis, invasion, and proliferation, making it an important therapeutic target.
Hence, the effort to find new anti-cancer agents with better efficacy and fewer side effects by
inhibiting the expression of SUMOylation is of major interest. Earlier studies had demonstrated
that Anacardic acid (AA), a natural compound from cashew nut shell liquid, exhibited an IC50 of
2.1 µM against small ubiquitin-like modifier E1 (SUMO E1) but had limitations of high
lipophilicity and low bioavailability. The present study is therefore focused on understanding the
Structure Activity Relationship studies (SAR) of AA derivatives against SUMO E1 in order to
identify potential chemotherapeutic agents with enhanced pharmacokinetic properties. The
binding energy, inhibition constant, and binding modes of AA derivatives have been studied using
molecular docking. Select 129 derivatives of Anacardic acid were screened utilizing molecular
docking with AutoDock 4.0. Of these, 61 potential compounds were selected based on their
binding energy. Secondary screening was subsequently carried out using the SwissADME virtual
platform, where pharmacokinetic properties such as solubility, logP and toxicity were evaluated.
This process resulted in the identification of 24 lead compounds, of which the top hits included 2-
(Carboxymethyl)-6-hydroxybenzoic acid, 2-(7-Carboxyheptyl)-6-hydroxybenzoic acid, 2-[3-(2,5-
Dimethyl-phenyl)-propyl]-6-methoxy-benzoic acid, 2-Methoxy-6-(5-phenyl-pentyl)-benzoic acid
and 2-Hydroxy-6-(2-naphthalen-2-yl-ethyl)-benzoic acid with enhanced binding energy and
Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties against
SUMO E1.
Keywords: Anacardic acid, SUMOylation, SAR studies, Chemotherapeutic agen, SUMO E1
Indian Conference on Bioinformatics 2023 - Inbix'23
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Computational Resources for Understanding and Predicting the
Binding Affinity of Protein- Nucleic Acid Complexes
Harini K and Michael Gromiha*
Indian Institute of Technology, Madras, Tamil Nadu,
gromiha@iitm.ac.in *
Abstract
Rationale/Motivation: Protein-nucleic acid interactions are inevitable in maintaining the
homeostasis of cells. It is vital to have a quantitative understanding of these interactions, generally
described in terms of the dissociation constant or free energy change of protein-DNA and protein-
RNA complexes. With the increase in the experimental data, there was no well-curated database
available specific for protein-nucleic acid-binding affinity. In addition, the availability of the
experimental data also affects the performance of the binding affinity prediction method.
Computationally, protein-DNA binding affinities are predicted using molecular dynamics
simulations, statistical methods, and machine learning techniques. Most of these methods are
focused on a specific protein-DNA complex or a small set of data. In addition, the performance of
the available methods is not uniform in different structural and functional classes of protein-DNA
complexes.
Objectives: Hence, we developed a database, ProNAB, which contains more than 20,000
experimental data for the binding affinities of protein-DNA and protein-RNA complexes. Further,
we developed a web server, PDA-Pred (Protein-DNA Binding affinity predictor), for predicting
the affinity of the protein-DNA complexes.
Materials and Methods: We obtained experimental binding affinity data from a detailed survey
of literature and existing/ obsolete databases. We have retrieved research articles and reviews on
the binding affinity of protein-nucleic acid complexes using keyword searches. From each article,
we manually curated the information about the name of the protein, nucleic acid, complex,
experimental conditions, measurement, method, thermodynamic data, literature information, and
location of the data in the research article. Each entry in ProNAB is cross-linked with GenBank,
UniProt, PDB, ProThermDB, PROSITE, DisProt and Pubmed. It provides a user-friendly web
interface with options to search, display, visualize, download and upload the data.
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Further, we filtered the binding affinity of protein-DNA complexes from the developed database
using the following criteria: (i) experimentally known binding affinity (ΔG), (ii) known 3D
structure, and (iii) non-redundant complex structures and obtained 391 protein-DNA complexes.
We obtained several structure-based features such as interaction energy, contact potentials,
volume, surface area of binding site residues, base step parameters of the DNA, and contacts
between different types of atoms. We developed multiple regression equations for predicting the
binding affinity of protein-DNA complexes belonging to different structural and functional classes
of protein-DNA complexes.
Results and Discussion: Our analysis of the relationship between binding affinity and structural
features revealed that the critical factors mainly depend on the number of DNA strands and
functional and structural classes of proteins. Specifically, binding site properties such as the
number of atom contacts between the DNA and proteins, volume of the protein binding sites, and
interaction-based features such as interaction energy and contact potentials are essential to
understand the binding affinity. Our method showed an average correlation and mean absolute
error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding
affinities on leave-one-out cross-validation (jackknife test).
Data availability: ProNAB, which is freely available at https://web.iitm.ac.in/bioinfo2/pronab/
(Harini et al., 2022). PDA-pred is available at https://web.iitm.ac.in/bioinfo2/pdapred/. (Harini et
al., 2023)
Conclusions: We have developed a database that would aid researchers in gaining insights for
understanding the relationship among binding affinity, structure, function, and diseases. Further,
we have developed a web server for predicting the binding affinity of protein-DNA complexes,
and it will be helpful for large-scale analysis and devising strategies for therapeutic targets.
Keywords: protein–DNA complex, binding free energy, contact potentials, structure-based
features
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Tackling Drug Resistance in Glioma by Targeting mIDH2R140Q Protein:
A Computational Repositioning Strategy
Poornimaa Murali and Ramanathan Karuppasamy*
Vellore Institute of Technology,
kramanathan@vit.ac.in *
Abstract
Rationale: The Isocitrate Dehydrogenase (IDH) mutation is a hallmark of early gliomagenesis
that significantly impacts various human malignancies. Presently, the first-in-class IDH2R140Q
inhibitor, AG-221 (enasidenib), has a remarkable selectivity against the target with an IC50 value
of 100 nM. However, acquired resistance due to the standard of care treatment and its inability to
cross the blood-brain barrier has restricted its use as a mIDH2 inhibitor. Additionally, indirect
hyperbilirubinemia and differentiation syndrome were the most frequent side effects of the
treatment interventions.
Objective: The prevailing obstacles necessitate the need to develop more potent and selective
inhibitors against the mIDH2 protein.
Materials and Methods: In the current study, an integrated virtual screening pipeline was adopted
to scrutinize effective compounds from the approved subset of the DrugBank library containing
2715 compounds. The binding characteristics of the compounds were estimated using molecular
docking, MM-GBSA analysis and mutational analysis. Further, compounds with enhanced binding
affinity were revalidated using machine learning-based (ML) scoring functions. Finally, the
conformational sturdiness of the lead molecule was reassured by performing a molecular dynamics
(MD) simulation study for 100 ns.
Results and Discussions: A total of 27 compounds were scrutinized from an integrated pipeline
with appreciable binding affinity, ΔGbind against the mutational variants. The binding energy of
the screened compounds varied from -62.27 kcal/mol to -38.55 kcal/mol. Of note, DB00872
(Conivaptan) exhibits better binding affinity (-42.98 kcal/mol) and a satisfactory toxicity profile.
The compound also displayed score values of 6.123 pK units, 0.49 µM, 6.93 kcal/mol and -8.85
CNN affinity in RF, NN, KDEEP and GNINA respectively. The MD simulation study also
affirmed the conformational sturdiness of the hit molecule.
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Conclusion: We are certain that the outcome of our study will be of immense importance for
managing enasidenib resistance in gliomagenesis.
Keywords: Mutated Isocitrate Dehydrogenase2, MM-GBSA calculation, machine learning,
molecular dynamic simulations, drug repurposing
Indian Conference on Bioinformatics 2023 - Inbix'23
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Drug Repurposing Strategies for the Management of Triple Negative
Breast Cancer: Focus on Indoleamine 2, 3-Dioxygenase and
Tryptophan-2, 3 Dioxygenase Targets
Priyanga Paranthaman and Shanthi Veerappapillai*
Vellore Institute of Technology,
shanthi.v@vit.ac.in *
Abstract
Rationale: The kynurenine pathway (KP) plays a pivotal role in the dampening of the immune
response in many types of cancer, including TNBC. The intricate involvement of tryptophan
degradation via KP serves as a critical regulator in immune-privileged regions through the aberrant
expression of key enzymes such as indoleamine 2,3-dioxygenase (IDO1) and tryptophan 2,3-
dioxygenase (TDO). Despite the availability of navoximod, its poor bioavailability and inadequate
efficacy in clinical trials have hampered its utility.
Objectives: In the present study a novel and potent dual-target inhibitor was developed against
the vital enzymes, IDO1 and TDO.
Materials and Methods: The investigation employed a comprehensive pipeline of molecular
docking and dynamic simulations to evaluate the binding stability of the lead compounds.
Results and Discussion: A total of 2588 compounds from the approved subset of the DrugBank
database were proclaimed and subjected to a preliminary evaluation of their toxicity and
pharmacokinetic properties. Subsequently, hierarchical molecular docking, prime MM-GBSA and
integrated machine learning algorithms precisely identified the potential lead compounds. The
antineoplastic activity of the hit compounds was also estimated using the PaccMann server, with
values ranging from 0.203 to 24.119μM. Collectively, the results of the hit compound DB06292
strongly reinforced its candidature as an effective anti-cancer agent. Finally, the reliability of the
results was corroborated through a rigorous 100 ns molecular dynamics simulation, ensuring the
stable binding of the hit against the target proteins.
Conclusions: Considering the favorable outcomes, experimental studies on the proposed hit
compound hold promising capabilities in the treatment and management of TNBC.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Keywords: Indoleamine 2 3-dioxygenase (IDO1), Tryptophan 2 3-dioxygenase (TDO), TNBC,
Molecular docking, MM-GBSA, Machine learning, Molecular dynamics simulation
Indian Conference on Bioinformatics 2023 - Inbix'23
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In-Silico Design of Antimicrobial Peptides from Bungarus Caeruleus
and Molecular Docking & Dynamics Simulation Against
Mycobacterium Tuberculosis
Priyanka Singh (Vellore Institute of Technology) and Jayaraman Gurunathan (Vellore Institute
of Technology)
gjayaraman@vit.ac.in
Abstract
Rationale: Antimicrobial resistance is the key threat to global health due to high morbidity and
mortality. The alteration of bacterial proteins, enzymatic degradation, and change of membrane
permeability towards antimicrobial agents are the key mechanisms of antimicrobial resistance.
Based on the current condition, there is an urgent clinical need to develop new drugs to treat these
bacterial infections. Several studies related to snake venom components have shown to be
promising molecules having the ability to interfere with the biofilm formation of bacteria., a study
citing the effect of viper snake venom PLA2, an enzyme that is involved in the hydrolyzing of
phospholipids of membrane acting as a major protein component in the Bothrops erythromelas
has shown to inhibit the formation of biofilm against Acinetobacter baumanii. Considering the
importance of snake venom proteins, we have designed a snake venom PLA2-derived
antimicrobial peptide against the drug-resistant Mycobacterium tuberculosis.
Objectives: (1) Prediction, design and characterization of antimicrobial peptides from Bungarus
caeruleus using an in-silico approach. (2) Molecular docking and dynamics simulation for the
predicted AMPs against Mycobacterium tuberculosis target protein.
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Materials and Methods: Prediction of antimicrobial peptides from B. caeruleus by digesting the
proteome sequences using different enzymes from the EXPASY PEPTIDE CUTTER tool. Then,
digested peptide sequences were subjected to the DBAASP server for the identification of being
antimicrobial peptide properties. Then, the physicochemical properties of the peptides were
analyzed using the PROTPARAM and APD3 database. Further, the ADMET property of the
predicted AMPs was evaluated using the ADMETLAB2.0 server, before the prediction, the
SMILES character of peptides was predicted using the PEPSMI server. Then, the structure of the
AMPs was predicted using the PEPFOLD3.0 server where the best peptide model was taken for
the molecular docking study against M. tuberculosis protein (PDB ID - 5NIO), an EthR
transcriptional repressor protein using the HADDOCK tool. Then molecular dynamics simulation
was performed for the complex with the maximum binding affinity of protein-peptide complex
for 100 ns using GROMACS-2023.1 version. Finally, MMPBSA analysis was analyzed for the
enthalpy energy and other interaction energy.
Results and Discussions: Protein sequences were retrieved from the UniProt database for the
Bungarus caeruleus (Indian Krait) and subjected to expasy peptide cutter tool for cleavage of larger
protein sequences into shorter peptide sequences using different enzymes such as Chymotrypsin
(high and low specificity), Tryspin, and Pepsin (pH 1.3 and >2). From the server, we predicted 41
peptide sequences to have antimicrobial properties, for which prediction was performed based on
the machine learning algorithm and used the Moon and Fleming scale. Based on the
physicochemical properties, peptides were further narrowed down to 11 numbers based on stability
and desirable ADMET properties. Docking analysis revealed that the peptide
HGATVAVKQVNRCSKNHL effectively binds to the target protein of M. tuberculosis. Docking
results were validated using molecular dynamics simulation using GROMACS-2023.1 version and
MMPBSA analysis was also performed.
Conclusions: The results of this computational approach support the evidence of the efficiency of
these AMPs as potent inhibitors of the specific proteins of Mycobacterium tuberculosis. However,
further in-vitro validations are required to fully evaluate the potential of selected AMPs as drug
candidates against Mycobacterium tuberculosis.
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In-Silico Approach to Explore Anticancer Properties in Gloriosa
Superba Derived Compounds Against Prostate Cancer
Aswathi P and Renuka Suravajhala*
Amrita School of Biotechnology,
renus@am.amrita.edu*
Abstract
Background: Prostate cancer develops in the prostate gland; all men are at risk of developing
prostate cancer. About one in nine men will be diagnosed with it in their lifetime, but only one in
39 men will die from the disease. About 80 percent of men in their 80s have prostate cancer cells.
Recent studies show an increase in the incidence of prostate cancer in young men. Prostate cancer
in the younger age group is usually undifferentiated and has a poor prognosis.
Objectives: In the present study, 14 pathogenic prostate cancer targeted proteins and considered
to investigate molecular interactions with gloriosa superba derived phytochemicals such as
(Gloriosine, Colchicine,3-Demethyle gloriosine,3-Demethyle colchicine) and control as 5-
Fluorouracil (5-FU).
Materials and Methods: Autodock4 is used for the docking of prostate cancer proteins with
gloriosa superba derived compounds. The hydrogen bond and pi-pi interactions of the targeted
proteins with ligand is observed by discovery studio and further complex visualizations are
observed by Chimera.
Results and Discussion: We have identified Gloriosine, Colchicine,3-Demethyle gloriosine,3-
Demethyle colchicine are having –6 to –8 binding energy towards targeted protein (pdb id:
2b2h,3lmy,5ctg and 7m81). We identified the possible hydrogen bonding (LYS A: 254, GLN A:5,
GLY A;4, GLN:C207, GLY A:114) with respect to the proteins 7M81 and 5CTG.
Future perspective: We will further go for Insilco studies and Molecular Dynamic Simulations
with the selected significant proteins.
Keywords: Prostate cancer, Gloriosa superba, Molecular Dynamics
Indian Conference on Bioinformatics 2023 - Inbix'23
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Proteome-Wide Scanning Approach to Detect rpIE as a Novel
Therapeutic Target of M.ulcerans
Harshini Senthilkumar, Tamil Barathi P, Mohanapriya Arumugam*
Department of Biotechnology, School of Biosciences and Technology, Vellore
Institute of Technology, Tamil Nadu, India,
mohanapriyaa@vit.ac.in*
Abstract
Buruli ulcer is caused by Mycobacterium ulcerans, the third most prevalent bacterial disease after
leprosy and tuberculosis. The rise of bacterial strains resistant to treatment raises severe concerns
and highlights the need for improved therapies because the current treatment options are limited.
Recent developments in whole- genome sequencing combined with chemotherapy, computational
biology, and experimental research represent a compelling alternative strategy for identifying
deserving therapeutic candidates for treatment. The KEGG Genome database is employed to
comprehensively map the metabolic pathways of both the pathogen and the host. Additionally,
NCBI BLAST is used to compare essential biological sequence information, while the DEG
database assists in identifying dominant proteins within unique and common pathways. The String
database aids in the exploration of protein associations and interactions, and the CytoHubba
software is exploited to identify the most critical pathogenic drug targets. Finally, rpIE is the most
prominent and important node, ranking highest for its centrality measure. This gene has been
identified as the main hub gene that could represent the target of a future medication. The hub
protein (rpIE) of the Mycobacterium ulcerans has been docked with phytochemicals, which have
demonstrated strong inhibitory potential energies. This information can lead to significant
advances in testing the efficacy of existing antibiotics compared to new antibiotics, manufacturing
drugs with minimal host toxicity, and developing vaccines.
Keywords: Mycobacterium ulcerans, Buruli ulcer, Genome analysis, Hub proteins, Drug-target.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Computational Identification of Biomarker Genes for Hormone-
Sensitive Cancers Considering Treatment and Non-Treatment Studies
– A Meta-Analysis Approach
Tamizhini Loganathan and George Priya Doss C*
Laboratory of Integrative Genomics, Department of Integrative Biology, SBST, Vellore Institute
of Technology,
georgepriyadoss@vit.ac.in *
Abstract
Breast, ovarian, and endometrial cancers majorly impact women's mortality. These tumors share
hormone-dependent mechanisms in female-specific cancers, which support tumor growth
differently. Integrated computational approaches may allow us to better detect genomic similarities
between these female-specific cancers, helping us deliver more sophisticated diagnoses and
precise treatments. This study aims to computationally identify biomarker genes for hormone-
sensitive cancers that can aid their diagnosis and treatment. The gene expression profiles of two
different types of studies, namely non-treatment and treatment, are considered for discovering
biomarker genes. In non-treatment studies, healthy samples are control, and cancer samples are
cases. In treatment studies, controls are cancer cell lines without treatment, and cases are cancer
cell lines with treatment. The Differentially Expressed Genes (DEGs) for hormone-sensitive
cancers were isolated from the Gene Expression Omnibus (GEO) database, and the datasets were
analyzed using the online tool IMAGEO. Two Cytoscape apps, CytoHubba and MCODE, were
used to identify the hub genes from functional networks using overlap genes from different meta-
analysis using MCC and particular hub gene method. Most of the biomarker genes from non-
treatment studies are part of mitosis and play a vital role in DNA repair and cell-cycle regulation.
In contrast, most of the biomarker genes from treatment studies are associated with cell cycle and
cellular senescence. This study discovered a list of biomarkers to help experimental scientists
design a lab experiment to further explore the detailed dynamics of female-specific cancer
development.
Keywords: Breast Cancer, Ovarian Cancer, Endometrial Cancer, Meta-analysis
Indian Conference on Bioinformatics 2023 - Inbix'23
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Investigation of the Impact of R273H And R273C Mutations on the
DNA Binding Domain of P53 Protein Through Molecular Dynamic
Simulation
Hephzibah Cathryn.R and Dr. George Priya Doss C* Laboratory of Integrative Genomics,
Department of Integrative Biology, SBST, Vellore Institute of Technology,
georgepriyadoss@vit.ac.in *
Abstract
The P53 protein, a cancer-associated transcriptional factor and tumor suppressor, houses a Zn2+
ion in its DNA-binding domain (DBD), essential for sequence-specific DNA binding. However,
common mutations at position 273, specifically from Arginine to Histidine and Cysteine, lead to
a loss of function as a tumor suppressor, also called DNA contact mutations. The mutant (MT)
P53 structure cannot stabilize DNA due to inadequate interaction. To investigate the
conformational changes, we performed a comparative molecular dynamic simulation (MDS) of
1000 ns to study the effect of the P53-Wildtype (P53-WT) and the DNA contact mutations (R273H
and R273C) on the DBD. Our research indicated that the DNA binding bases lose Hydrogen bonds
(H bonds) when mutated to P53-R273H and P53-R273C during the simulation. We employed tools
such as PDIviz to highlight the contacts with DNA bases and backbone, major and minor grooves,
and various pharmacophore forms of atoms. The contact maps for R273H and R273C were
generated using the COZOID tool, which displayed changes in the frequency of the amino acids
and DNA bases interaction in the DNA binding domain. These residues have diminished
interactions, and the zinc-binding domain shows significant movements by Zn2+ ion binding to
the phosphate group of the DNA, moving away from its binding sites. In conclusion, our research
suggests that R273H and R273C each have unique stability and self-assembly properties. This
understanding might assist researchers in better comprehending the function of the p53 protein and
its importance in cancer.
Keywords: P53, cancer, DNA contact mutations, DNA-binding domain, Zinc Binding domain,
Molecular Dynamic Simulations
Indian Conference on Bioinformatics 2023 - Inbix'23
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Evaluation of Ocimum Basilicum for its Antifibrotic and Drug-Likeness
Properties – A Computational Pharmacology Approach
K Gayathri, N Malathi, V. Gayathri, J. Jino Blessy and P. A. Abhinand
Sri Ramachandra Institute of Higher Education and Research
gayathri.researcher@gmail.com
Abstract
Oral submucosal fibrosis (OSMF) caused by areca nut chewing is an insidious disorder with the
potential to become malignant. The fibrosis of oral mucosa leads to trismus which affects the
quality of life. The high occurrence of OSMF in India coupled with the non-availability of a
complete cure poses a significant health challenge. Traditionally used herbs possess excellent
pharmacological properties and could be re-purposed to treat incurable diseases. Bioinformatics
helps expedite the identification of potential drug candidates for disease. The present study aims
to identify drug-like phytocompounds of Ocimum basilicum with antifibrotic activity by pathway
analysis and gene ontology (GO) analysis. Seven ligands were identified by virtual screening of
compounds present in the herb. 520 potential targets were identified for the drug-like compounds
and 354 targets were identified for OSMF. Among these targets, 49 common proteins were
identified. GO analysis and KEGG pathway analysis of the common proteins reveal various
pathways involved in the pathogenesis of OSMF. Network analysis of the PPI network comprising
of 49 common proteins identified four key proteins involved in OSMF pathogenesis viz;
transforming growth factor- β1 (TGF-β1), epidermal growth factor receptor (EGFR), caspase-3
(CASP-3) and hypoxia-inducible factor-1 (HIF-1A). These findings demonstrate the antifibrotic
activity of Ocimum basilicum.
Keywords: Oral submucous fibrosis, Ocimum basilicum, Pathway analysis, GO analysis,
Computational pharmacology
Indian Conference on Bioinformatics 2023 - Inbix'23
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Systems and Computational Screening Identifies SRC and NKIRAS2 As
Baseline Correlates of Risk (Cor) for Live Attenuated Oral Typhoid
Vaccine (TY21a) Induced Protection: An In-Silico Pipeline
Akshayata Naidu a,1, Varin Garg a,1, Deepna Balakrishnan a,2, Vinaya C.R a,2, Vino
Sundararajan* a, and Sajitha Lulu S* a,
aIntegrative Multiomics Laboratory, School of Bio Sciences and Technology, Vellore Institute of
Technology, Vellore, 632014, Tamil Nadu,
ssajithalulu@vit.ac.in *, svino@vit.ac.in *
Abstract
Rationale: Typhoid is a major public health concern, and the live attenuated typhoid vaccine
(Ty21a) is one of the most widely used vaccines against this disease. However, there is significant
variation in immune responses to Ty21a, and it is important to identify biomarkers that can predict
vaccine responsiveness at baseline.
Objectives: (1) To investigate the molecular basis of variance in immune responses to Ty21a by
exploring the baseline immune landscape. (2) To identify potential biomarkers associated with
Ty21a vaccine responsiveness at baseline using two distinct computational approaches:
Knowledge-based approach: retrieval of differentially expressed genes (DEGs), functional
enrichment analysis, protein-protein interaction network construction and topological network
analysis of post-immunization datasets before gauging their pre-vaccination expression levels.
Data-driven approach: unsupervised machine learning algorithm for data-driven feature selection
on pre-immunization datasets. (3) To computationally validate identified biomarkers using
supervised machine learning classifiers.
Methods:
• Knowledge-based approach
o Retrieval of differentially expressed genes (DEGs) from post-immunization datasets using the
GEO database (GSE100665).
o Functional enrichment analysis of DEGs to identify enriched biological pathways and processes.
o Protein-protein interaction (PPI) network construction of DEGs.
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o Topological network analysis of the PPI network to identify hub genes.
o Gauging the pre-vaccination expression levels of hub genes.
• Data-driven approach
o Unsupervised machine learning algorithm for data-driven feature selection on pre-immunization
datasets.
o Supervised machine learning classifiers to validate identified biomarkers.
Results: The knowledge-based approach identified three genes (NKIRAS2, SRC, and
LOC100134365) that were differentially expressed between vaccine responders and non-
responders at baseline. The data-driven approach also identified these three genes as potential
biomarkers. Supervised machine learning classifiers using the three identified genes were able to
accurately distinguish vaccine responders and non-responders, with 88.8%, 70.3%, and 85.1%
accuracy for NKIRAS2, SRC, and LOC100134365, respectively.
Conclusions: This dual-pronged novel analytical approach provided a comprehensive comparison
between knowledge-based and data-driven methods for the prediction of baseline biomarkers
associated with Ty21a vaccine responsiveness. The identified genes shed light on the intricate
molecular mechanisms that influence vaccine efficacy from the host perspective while pushing the
needle further toward the need for the development of precise enteric vaccines and the importance
of pre-immunization screening.
Keywords: Ty21a vaccine, Immunological profiles, Differentially Expressed Genes (DEGs),
Machine learning classifiers, Vaccine responsiveness
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Association of CTLA-4 Signal Peptide (T17A) Polymorphism with
Rheumatoid Arthritis in the Indian Population: A Case-Control Study
and In Silico Analysis
Shamala V And Asha Devi S,
Vellore Institute of Technology
sashadevi@vit.ac.in
Abstract
Motivation: The Cytoplasmic T-lymphocyte-associated antigen 4 (CTLA-4) Rs231775 signal
peptide single nucleotide polymorphism (SNP) was targeted and screened for the association of
Rheumatoid arthritis (RA) in the Indian population. An insilico approach to predict that CTLA-4
Rs231775 SNP in signal peptide has been efficiently affecting the transportation of CTLA-4
polypeptide chain into the endoplasmic reticulum (ER) by disrupting SRP 54 (Signal Recognition
Particle - M domain) protein interaction.
Materials and Methods: The CTLA-4 Rs231775 SNP were genotyped by High-Resolution
Melting Analysis (HRMA). The confirmation of SNP was done by Sanger’s sequencing. Various
in silico tools SignalP 6.0, ConSurf, InterPro, ProtParam, Project HOPE, RNA fold, RNA 3D
Composer, NetSurfP 3.0, and SOPMA were used to predict the signal peptide structure and
functions. ClusPro 2.0 for Protein-Protein docking and Google Colab for Molecular Dynamic
(MD) Simulation.
Results and Discussion: The CTLA-4 Rs231775 SNP AG and GG genotypes are associated with
RA susceptibility in the Indian population. An insilico study reveals CTLA-4 Rs231775 SNP (G
allele) significantly affects the stability and folding pattern of mRNA structure. In addition,
molecular docking and MD simulation reveal that Rs231775 SNP disturbs the SP-SRP recognition
pattern, which affects the translocation of CTLA-4 nascent polypeptide chains into ER via the
RAPP pathway.
Conclusion: Despite Rs231775 SNP found on signal peptide rather than mature protein, it has a
significant impact on CTLA-4 gene expression. We conclude that SP-SRP interaction is important
for successful mRNA stability and CTLA-4 protein translation, preventing various autoimmune
diseases, especially RA.
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Keywords: Rheumatoid arthritis; CTLA-4 gene; Rs231775 SNP; HRMA; Signal Recognition
Particle.
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Logical Modelling of Gene Regulatory Circuits Involved in CCL20
Induction in Human Organoids Using Systems and Computational
Analysis of RNA seq Dataset
Akshayata Naidu, Mohak Bhattacharyya, Dhanya N, Arah Paras Bora, Tejoram Sambrani, Vino
Sundararajan* and Sajitha Lulu S1*
aIntegrative Multi Omics Laboratory, School of Bio Sciences and Technology, Vellore Institute of
Technology, Vellore, 632014, Tamil Nadu
ssajithalulu@vit.ac.in, svino@vit.ac.in
Abstract
Rationale: Protective immune responses against mucosal infections are extremely difficult to
evaluate due to inaccessibility of clinical mucosal samples and gap in the understanding of the
cross-talk between systemic and mucosal immune responses. Hence, establishment of robust co-
relates of protection (CoP) becomes crucial to study vaccine induced immune responses during
pre-clinical and clinical trials.
Objective: • To retrieve highly influential mediators involved in the cross-talk between mucosal
and systemic immune responses upon infection. • To derive key upstream and downstream
regulators of the highly influential genes for the construction of dynamic logical models.
Method: A two-tiered comprehensive analysis of gene expression dataset where gene expression
values were derived post-stimulation with different microbes and microbial components.
Differentially expressed genes (DEGs) were retrieved and functional enrichment analysis was
performed. Thereafter, a curated list of genes was used to construct protein-protein interaction
(PPI) static network using STRING database for topological network analysis for identification of
hub genes. Mediators associated with the hub genes were identified using Pearson correlation co-
efficient and were further validated using multi-variant regression analysis. The derived multi-
variant regression models were translated as logical model in order to establish and analyze
dynamic network models associated with induction of mediators involved in the transition from
mucosal to systemic immune responses in the human organoids.
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Results: Topological Network analysis along with literature-based validation revealed CCL20 as
a prominent lympho-attractant and hence was taken ahead for the analysis as a CoP candidate. The
second tier of the study unveiled major determiners of CCL20 induction which includes interferon
alpha and interferon gamma receptors along with IL7, IL17C, IL1B, IL1A, IL20 and IL10RB-DT.
Moreover, key regulators of the derived features were found to be RELA, NFKB1, JUN, SP1,
STAT3, E3F1, STAT1, HMGA1. The identified mediators (30) and their regulators were further
used for the construction and analysis of dynamic network after the construction of the truth table.
Conclusion: Systems and computational analysis of RNASeq dataset reveal key mediators and
associated regulatory modules involved in the crosstalk of mucosal and systemic immune
responses which can act as potential correlates of protection (CoP) against mucosal infections.
Keywords: CCL20, Logical modelling, Co-relates of protection, RNASeq data, Mucosal
immune response
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Does Metabacillus Halosaccharovorans Possess Inherent Radiation
Resistance? A Comparative Genome Approach
Sowptika Pal and Anilkumar Gopinathan *, School of Biosciences and Technology, Vellore
Institute of Technology, Vellore, Tamil Nadu, India,
ganilkumar@vit.ac.in *
Abstract
VITHBRA001, a strain identified as Metabacillus halosaccharovorans, was isolated from
Chavara-Neendakara placer deposit- a high background radiation area situated in Kerala, India.
The organism was experimented with induced gamma radiation and was found to be radiation
resistant with D10 value of 2.42 kGy, surviving 5 kGy of gamma radiation. Whole genome
sequencing study was performed to analyze the radiation resistant genes present in this bacterium
and so far, there is no report of this bacterium being radiation resistant. It makes us curious to
know if the strain VITHBRA001 has acquired the properties of radiation resistance or this species
inherits the resistance capacity. In order to achieve this understanding, we tried to compare the
genome of its 4 strains with the help of functional characterization using COG analysis for the
entire genome, and with segregated core, accessory and unique proteins of these strains. It could
be observed that strains MET-TA-181 and B410 had higher number of proteins in almost all the
COG categories including carbohydrate transport and metabolism (G), replication, recombination
and repair (L), nucleotide transport and metabolism (F), inorganic ion transport and metabolism
(P), cell cycle control, cell division, chromosome partitioning (D) and cell
wall/membrane/envelope biogenesis (M) which are reported to be important in radiation
resistance. VITHBRA001 and DSM25367 showed comparable gene number in these categories
but in-dept analysis shows that DSM 25367 has more of accessory genes while others housing
more unique genes. The present assessment encourages us to hypothesize that VITHBRA001, in
spite of having the smallest genome of the 4 and lesser number of resistance genes as compared to
others, has shown radiation resistance to the tune of 5 kGy. Thus, we are tempted to pose the
question if the members of this species may have inherent radiation resistance capabilities.
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Keywords: Metabacillus halosaccharovorans, Radiation resistant bacteria, High Background
Radiation Area, COG
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A Multi-Objective Hybrid Machine Learning Approach-Based
Optimization for Enhanced Biomass and Bioactive Fucoxanthin
Production in Isochrysis Galbana
Janani Manoch Kumar 1, Ashwani Kumar Cherukuri 2*, Annapurna Jonnalagadda 3, and Siva
Ramamoorthy 1*
1 Professor and Dean, School of Bio Sciences and Technology, Vellore Institute of Technology,
rsiva@vit.ac.in
2 School of Information Technology and Engineering, Vellore Institute of Technology,
aswani@vit.ac.in
3 School of Computer Science & Engineering, Vellore Institute of Technology,
jannapurna@gmail.com
Abstract
Rationale of the study: To develop a multi-objective hybrid machine learning-based optimization
approach that is fast, robust, scalable and provides automated analytical method for enhanced cell
biomass and fucoxanthin production simultaneously in Isochrysis galbana. This study aims to
identify the cost-effective optimal production of microalgal pigment within limited time using
machine learning algorithms.
Objectives: 1. To analyse and understand the behavioral characteristics and pattern of data using
exploratory data analysis. 2. To optimize the concentration of phytohormone for enhanced
fucoxanthin production in Isochyris galbana culture. 3. To predict the yield of fucoxanthin when
the concentration and number of days was given as input. 4. To assess and validate the multi-
objective hybrid machine learning algorithm for enhanced biomass and pigment yield.
Materials and Methods: Development of a machine learning based model by feeding the
experimental responses of spectrophotometric data depicting the growth rate, biomass and
fucoxanthin production from microalgal cultures supplemented with various concentrations of
phytohormones including Salicylic acid, Methyl jasmonate, Giberrelic acid, and Indole Acetic
Acid.
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Results and Discussion: Fucoxanthin is a valuable carotenoid with a high market value in the
pharmaceutical and nutraceutical industries. Thus, the variables for enhanced fucoxanthin
production and biomass in microalgae were optimized using statistical models and a feed-forward
neural network-based machine learning algorithm with two hidden layers. This study highlights
the advantages of using a machine learning approach to achieve optimized pigment production and
serves as the foundation for future efforts to convert microalgae as an economically viable source
for large-scale production of pigment.
Conclusion: A hybrid machine learning model can accurately and precisely predict the optimized
biomass and fucoxanthin production in microalgal culture. The proposed model would aid the
rapid estimation of pigment yield from microalgal culture that may reduce laborious, expensive
and time-consuming laboratory trial experiments as well as making it a reliable and standardized
method.
Keywords: Machine learning, Enhanced fucoxanthin, Microalgae
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Phylogenetic Status of a Field Crab (Brachyura: Decapoda) From
Pedavedu, Thiruvannamalai (Tamil Nadu): An Integrative Approach
Through Molecular Taxonomy, Barcoding and Coding Matrix
Nishita Lal and Anilkumar Gopinathan* School of Biosciences and Technology, Vellore Institute
of Technology, Vellore, Tamil Nadu,
ganilkumar@vit.ac.in*
Abstract
This paper reports the results of evaluation of the phylogenetic status of a field crab which has
been referred to as Oziotelphusa species Muller, 1887, inhabiting the rice farms of Pedavadu,
Thiruvannamalai District (Tamil Nadu, India), using morphological and molecular parameters,
including bioinformatics softwares. There have been multiple descriptions of this newly
discovered Oziotelphusa species in recent times, however there is no evaluation for determining
the correct taxonomic classification of this brachyuran crab, especially in light of modern genetic
tools. The current work aims to review the taxonomic position of this field crab utilizing
morphological criteria, molecular approaches (COI and 18S rRNA gene sequences), barcoding
and coding matrix. Physiognomy of carapace and major appendages (including the cephalic
appendages, walking legs and abdominal appendages) was considered for creating the
morphological coding matrix and subsequent construction of phylogenetic tree. COI and 18S RNA
primed sequences were PCR amplified and characterized through DNA sequencer ABI 3130. The
sequence information was subjected BLAST and CLUSTAL alignments, construction of
phylogenetic tree and barcoding analysis with a view to obtain precise phylogenetic status of the
candidate specimen. This comprehensive study reveals the candidate specimen’s closer affinity to
superfamily Gecarcinucoidea, family Gecarcinucidae, sub-family Parathelphusinae and genus
Oziotelphusa. Comparisons of morphological and molecular characteristics with its closest
phylogenetic neighbors such as Oziotelphusa aurantia, O. bouvieri, O. stricta, O. biloba, O.
wagrakarowensis and O. kerala further suggest that the candidate specimen is a taxonomically
distinct entity, qualified to be considered an unreported species. Further, the paper views the
relevance of this taxonomic study from conservational angles as well.
Keywords: Brachyuran crabs, Gecarcinucidae, Oziotelphusa sp, molecular taxonomy, barcoding
Indian Conference on Bioinformatics 2023 - Inbix'23
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In Silico Subtractive Proteomics Analysis to Identify the Novel
Therapeutic Drug Targets Combating Antibiotic Resistance in Neisseria
Gonorrhoeae
Tamil Barathi Palanisamy, Nirmaladevi Ponnusamy, Faraz Ahmad and Mohana Priya
Arumugam*
Department of Biotechnology, School of Biosciences and Technology, Vellore
Institute of Technology, Tamil Nadu, India,
mohanapriyaa@vit.ac.in
Abstract
Gonorrhea, a sexually transmitted infection caused by the Gram-negative bacterium Neisseria
gonorrhoeae, remains a significant worldwide health concern, persisting despite ongoing
endeavors to eliminate it. In both genders, gonorrhea may manifest as urethritis in men and as
cervicitis or urethritis in women. Gonorrhea is manageable and can be effectively treated using
certain antibiotics. Nevertheless, the increasing prevalence of antibiotic-resistant strains of N.
gonorrhoeae is posing a growing challenge in treating gonorrhea, heightening the risk of it
becoming resistant to treatment. The current in-silico investigation seeks to discover potential
novel drug targets for combating N. gonorrhoeae infection, utilizing bioinformatics
methodologies. The foundational gene set encompasses 851 scrutinized proteins sourced from
seven distinct strains of N. gonorrhoeae, retrieved from the UniProt database. Furthermore, the
CD-HIT analysis identified unique sequences from the complete proteome. Subsequently, these
non-redundant proteins underwent a standalone Blast against the human proteome, resulting in the
identification of 232 proteins as non-homologous. Additionally, non-homologous and essential
proteins underwent scrutiny in the KEGG Pathway Database, resulting in the identification of six
distinct proteins. Furthermore, the subcellular localization of these specific proteins was verified,
and cytoplasmic proteins were selected for the analysis of druggability. Subsequent to that,
molecular docking was employed using PyRx software to assess a collection of FDA-approved
drugs from the DrugBank database. This screening aimed to evaluate their binding affinity with
newly identified druggable targets and receptor proteins. The top two compounds for each receptor
protein were chosen based on criteria such as binding affinity and the most favorable conformation.
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Lastly, analyses of absorption, distribution, metabolism, excretion, and toxicity (ADMET) were
executed using the SWISS ADME and Protox tools.
Keywords: gonorrhea, antibiotic resistance, CD-HIT, non-redundant proteins, KEGG pathway,
Binding affinity.
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Zero Inflated Conway-Maxwell Poisson Model: An Application to Cross-
Sectional Microbiome Data
Abhiram D. B.1, B. Binukumar1 and Gokulakrishnan Kuppan2
1Department of Biostatistics, 2Department of Neurochemistry, NIMHANS, Bengaluru
Abstract
This study investigates the efficacy of two zero-inflated regression models, namely the Zero
Inflated Negative Binomial Model (ZINB) and the Zero Inflated Conway-Maxwell Poisson Model
(ZICMP), in handling count-based microbiome data characterized by zero inflation. Utilizing a
secondary dataset from a NIMHANS study involving 60 subjects categorized into drug-naïve or
risperidone-treated Schizophrenia patients and healthy controls, the research focuses on 16s
ribosomal RNA (16s rRNA) gene sequence-based exploration of gut microbiome differences as a
potential non-invasive biomarker for Schizophrenia. To address overdispersion and excess zeroes
in the count data, both ZINB and ZICMP models were applied and evaluated based on criteria
such as Akaike Information Criteria (AIC), Vuong’s Test, and Rootogram plots. The results
indicate comparable performance between the two models, with similar AIC values of 190.5, and
negligible differences in Root Mean Squared Error (RMSE) values (288.56 for ZINB and 288.67
for ZICMP), suggesting similar predictive accuracy. In conclusion, both ZINB and ZICMP models
exhibit comparable fits and predictive performance for the examined microbiome dataset.
Keywords:16S ribosomal RNA, Microbiome, ZINB, ZICMP, Vuong’s Test, Rootogram, RMSE
Indian Conference on Bioinformatics 2023 - Inbix'23
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Insight Into the Bacterial Gut Microbiome of Penaeus vannamei Fed
with Functional Feed Additives Lactiplantibacillus plantarum by
Amplicon Sequencing
N. Lalitha*, K. Ambasankar, T. Sivaramkrishnan and P. N. Suganya ICAR- Central Institute of
Brackishwater Aquaculture, Chennai–600 028
lalitha.n@icar.gov.in, lalithaciba@gmail.com
Abstract
Rationale/Motivation: International shrimp aquaculture industry is booming rapidly, with
emphasis on minimizing antibiotic usage on shrimp farming. Aquaculture productivity is
intrinsically linked to health and the gut microbiota is rapidly emerging as a key an indicator of
shrimp health. Microbes which colonize constantly, known as the gut microbiome, communicate
to the host they inhabit and support a variety of essential host activities such as enzymatic digestion
and competitive exclusion of pathogens, thus enhancing the host immunity. Hence, gut
microbiome modulation is a promising idea for aquaculture and has been presented as a potential
replacement to the use of broad-ranging antibiotics in disease management.
Objectives: The present study was carried out to investigate the gut microbiome of the shrimp
following dietary feed additive of the L. plantarum probiotic and paraprobiotic in P. vannamei.
Materials and Methods: The experiment comprised of three treatment group, Group I fed with
L. plantarum probiotic 1011 CFU/g of the feed (LLP), Group II fed with L. plantarum
paraprobiotic (DLP) and Group III Control (CON) fed with basal diet without feed additive, in P.
vannamei for the period of 45 days. At the end of the experiment gut of the shrimp (n=3) was
collected from each group, DNA extracted and gut microbiome analysis was carried out using
Illumina Miseq sequencing of 16S RNA V3-V4 hypervariable regions.
Results: Alpha diversity (observed OTUs, Chao1 index, Simpson and Shannon index) were
evaluated and it was found that there exists no significant (P >.05) difference in diversity between
two diet groups and control (Observed OTUs ranged from 298 to 386.66 OTUs; Chao1 index
ranged from 307.42 to 416.05; Simpson index range from 0.93 to 0.97; Shannon index 6.18 to
7.26). There were no significant changes between the two diet groups for any of the indicators (P
>.05). The principal coordinate plots based on beta diversity index by unweighted unifrac analysis
Indian Conference on Bioinformatics 2023 - Inbix'23
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of gut of P. vannamei depicted distinct bacterial community profile between different treatments
with one sample in each group out layered. Current finding, regardless of diet, Proteobacteria have
been demonstrated to be the most prevalent phylum in the gut microbiome. Tenericutes,
Bacteroidetes, Planctomycetes, and Verrucomicrobia were also found but were not influenced by
the diet. Core microbiome analysis revealed, there have been 72 OTUs, 11 OTUs and 59 OTUs in
probiotic, paraprobiotic and control, respectively. Rhodobacteraceae 37.69% and
Flavobacteriaceae 15.74 %were beneficial core microbiome bacterial signatures observed
predominantly in the probiotic supplemented diet. Pseudoalteromonadaceae 10.64% observed
predominantly in the paraprobiotic group. Unique OTUs observed in the probiotic supplemented
diet Lutimonas1.68%, Ruergia 0.76%, Cellulomonadacea 0.44%. The number of OTUs at species
level specific to the probiotic, paraprobiotic, control, are 17(9.5%), 40 (22.3%) and 30 (16.8%),
respectively.
Discussion: In the current study, beta diversity depicts distinct gut bacterial community between
two different diets. Proteobacteria is the dominant phyla in the all the diets. Probiotic fed diet
showed dominance of Rhodobacteraceae and Flavobacteriaceae core microbiome whereas it was
Pseudoalteromonadaceae in paraprobiotic diet fed shrimp. Probiotic supplemented shrimp depicts
unique OTUs Lutimonas, Ruergia and Cellulomonadacea. The feed additive in the shrimp diet has
manipulated the gut microbiome with dominance of the beneficial bacteria and presence of unique
OTUs (Albores et al., 2017; Cheng et al., 2019; Xie et al., 2019; Landsman et al., 2019) compared
to diseased shrimp microbiota (Huang et al., 2020; Hou et al., 2018)
Conclusions: It was found that the probiotic supplementation modulated the host gut microbiome
with relative abundance of beneficial bacteria and unique bacterial taxonomic signatures in this
species.
Keywords: Gut Microbiome, Penaeus vannamei, Amplicon sequencing, Lactiplantbacillus
plantarum
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Poster Presentation
Evaluating the Performance of Machine Learning Methods for
Predicting Mortality in Intensive Care Unit Patients
Alexander (Young-Joon) Ahn1
1University of Wisconsin-Milwaukee
aahn5035@gmail.com
Abstract
Machine learning methods are increasingly being used for building diagnostic models in clinical
settings to identify patients who are at a higher risk of mortality. Recent studies have shown that
ensemble tree-based learning methods, provide an alternative non-parametric approach compared
to traditional methods for building predictive models in high-dimensional datasets. In this study,
we evaluated the performance of logistic regression, random forest, XGBoost, and LGBM (leaf-
wise tree-based learning algorithm) for identifying ICU patients with a 28-day mortality risk at
the time of hospital admission. The case study data originates from a subset of publicly available
data from the Medical Information Mart for Intensive Care (MIMIC) II database. The
performance of different methods was evaluated using prediction error curves. The results show
that the XGBoost classification method achieved the best prediction accuracy for classifying
survivors vs. non-survivors with (cross-validation area under the curve; AUC=0.86). The top
features for predicting death at the time of ICU admission included age, simplified acute
physiology score (SAPS), and serum sodium levels at admission. These results can help predict
which patients are likely to die within 28 days of ICU admission so that healthcare professionals
can design & implement optimal treatment strategies to improve patient outcomes. All analyses
were conducted using the AutoAI tool in IBM Watson Studio.
Keywords: Machine learning, mortality, predictive modeling, ICU
Indian Conference on Bioinformatics 2023 - Inbix'23
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Structure-Based Drug Designing Towards the Identification of
Potential Anti-Bacterial for Acinetobacter Baumannii by Targeting
Penicillin-Binding Protein
Soumya Ranjan Mahapatra, Jyotirmayee Dey, Namrata Misra, Mrutyunjay Suar
School of Biotechnology, KIIT Deemed to be University, Bhubaneswar-751024, India
soumyaranjanmahapatra685@gmail.com *
Abstract
According to the world health organization (WHO) reports, Acinetobacter baumannii is a
nosocomial bacterial pathogen and is responsible for a wide range of diseases including
pneumonia, necrotizing fasciitis, meningitis, and sepsis. The enzymes involved in the
peptidoglycan biosynthetic pathway are critical for the survival of this bacterium. PBPs remain
attractive targets for developing new antibiotic agents because they catalyse the last steps of the
biosynthesis of peptidoglycan, which is unique to bacteria and lies outside the cytoplasmic
membrane. The objective was to identify natural molecules that fit best at the substrate binding
pocket of the protein and interact with functionally critical residues. Here, we utilized the structure-
based virtual screening (SBVS) technique to identify the promising lead molecules against PBP
protein using computational approaches. During the High-throughput Virtual screening (HTVS)
analysis, we started with 25,372 molecules against the PBP model, among these; only 3284
molecules could be considered suitable for further steps. Finally, only sixty molecules were able
to pass Lipinski’s and ADMET properties. The four best hits were chosen based on docking scores.
Selected top-ranked each four compounds underwent molecular dynamics (MD) simulations for
100ns each to validate the docking interactions where all four compounds with zinc database id:
ZINC27742, ZINC30015, ZINC30764, and ZINC44583 are highly supported by root-mean square
deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and Hydrogen
bond analysis. Further, the MM/PBSA binding free energy analysis was performed for four ligands
bound PBP structure. From the study, we have found ZINC27742, ZINC30015, ZINC30764, and
ZINC44583 to be a potential inhibitor having all the characteristics of a promising drug candidate.
Keywords: Acinetobacter baumannii, Drug, Penicillin-Binding Protein, Virtual screening,
Molecular Dynamics simulations
Indian Conference on Bioinformatics 2023 - Inbix'23
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Immunoinformatics Aided Designing of a Next Generation Poly-Epitope
Vaccine Against Pseudomonas Aeruginosa Targeting Needle Tip
Protein
Jyotirmayee Dey, Soumya Ranjan Mahapatra, Namrata Misra, Mrutyunjay Suar School of
Biotechnology, KIIT Deemed to be University, Bhubaneswar
jmdey1997@gmail.com
Abstract
According to a World Health Organization report, Pseudomonas aeruginosa is one of the world's
most deadly organisms, producing hospital-acquired pneumonia, surgical infections, bacteremia,
and other potentially fatal diseases. However, no effective treatment or countermeasure to treat the
infection has yet been identified. The needle tip PcrV protein is an important protective antigen
against Pseudomonas infection. In this research, we used an immunoinformatics and molecular
docking approach to construct a multiepitope vaccine of eight conserved, highly antigenic, non-
allergenic, and non-toxic epitopes from needle tip protein to provide treatment against P.
aeruginosa infection. The selected epitopes were then joined together using suitable linkers, and
an adjuvant was added to the N-terminal to boost the immunogenicity of the vaccine. The vaccine
protein was further tested for allergenicity, antigenicity, and physiochemical characteristics, and it
was found to be safe and immunogenic. The best 3D model of the subunit vaccine was generated
using Robetta software. Disulfide engineering in a location of high mobility was used to improve
the stability of the vaccine protein. The modeled structure was successfully docked to antigenic
receptor TLR-4. To determine flexibility and conformational changes, a molecular dynamics
simulation of 100 ns was run. Increased levels of antibodies, INF-γ, IL-2, TGF-β, B-cells, CD4+,
and CD8+ cells were seen in immune simulation experiments, indicating the induction of primary,
secondary, and tertiary immune responses. Finally, to ensure vaccine expression and translation
efficiency within an expression vector, an in-silico cloning approach was used. The proposed
polyepitope subunit vaccine may be able to trigger both cellular and humoral immune responses.
However, experimental validation of the suggested construct is required to confirm its safety and
immunogenic profile.
Keywords: Pseudomonas aeruginosa, Vaccine, Bioinformatics, Molecular docking, MD
simulation
Indian Conference on Bioinformatics 2023 - Inbix'23
81
Unlocking the Molecular Landscape of Hepatocellular Carcinoma
Arising from Non-Alcoholic Fatty Liver Disease: WGCNA and Multi-
Omics approach
Subhajit Ghosh and Subarna Thakur
University of North Bengal
subho7jit@gmail.com
Abstract
Motivation: The prevalence of Non-alcoholic fatty liver disease (NAFLD) has surged in recent
years, closely paralleling the escalating rates of obesity and diabetes. NAFLD condition is
characterized by the accumulation of fat within liver cells. Though initially perceived as benign, it
is now considered to harbor the ominous potential to evolve into non-alcoholic steatohepatitis
(NASH), liver cirrhosis, and, ultimately, hepatocellular carcinoma (HCC). Significantly, the
associated risk of hepatocellular carcinoma (HCC) stemming from NAFLD has been chronically
underestimated, primarily due to its insidious progression and poor prognosis. In light of this, there
exists a compelling need to identify the key modulators that drive the transition of HCC from
NAFLD. By identifying these pivotal factors, we aspire to enable early diagnosis of this perilous
progression. Such early detection can help in prevention and treatment, potentially offering a
lifeline to countless individuals at risk of developing HCC as a consequence of NAFLD.
Objectives: This study aims to utilize Weighted Gene Co-Expression Analysis (WGCNA) to
pinpoint co-expressed genes linked to the transition from Non-Alcoholic Fatty Liver Disease
(NAFLD) to Hepatocellular Carcinoma (HCC), with a specific focus on identifying core hub genes
and unraveling their potential roles in HCC development through investigations of mutation
patterns, epigenetic modifications, miRNA-mRNA interactions, and tissue-specific protein
expression data. Additionally, this work intends to assess the prognostic potential of these core
hub genes by conducting survival analysis utilizing multivariate cox regression analysis.
Materials and methods: Gene expression datasets of three studies were utilized, comprising five
different stages – Control, healthy obese, steatosis or fatty liver, NASH, and HCC. The common
genes among these studies were used in the final data matrix, and after initial data normalization,
Weighted gene co-expression analysis (WGCNA) was employed for the identification of co-
Indian Conference on Bioinformatics 2023 - Inbix'23
82
expressed genes. Subsequently, a protein-protein interaction network (PPI) was utilized to identify
twenty-five hub genes in the co-expressed gene module for HCC. Next, Tissue-specific expression
information of the hub genes was analyzed. Concurrently, an exploration of mutation-associated
data and miRNA-mRNA interactions was carried out to unveil the underlying mechanisms
responsible for changes in gene expression. Additionally, to examine the prognostic potential of
these hub genes, survival analysis was done based on the multivariate cox regression method.
Results and Discussion: From WGCNA analysis results, the module with the highest positive
correlation with HCC showed that most of the genes are linked to vital metabolic pathways such
as Carbon metabolism, Oxidative phosphorylation, Diabetic cardiomyopathy, Glyoxylate and
dicarboxylate metabolism, and Fatty acid metabolism. The protein-protein interaction network
analysis was based on genes from the HCC module, which yielded 25 core hub proteins. Among
the 25 core hub proteins within the HCC module, genes such as ATP5F1A, ATP5F1B, ATP5F1C,
AGXT, EHHADH, UQCRC1, PCCB, ALDH9A1, ACADM, HSD17B4, FH, NDUFS2, MYC and
SDHB exhibited significant levels of expression in hepatic cells. Furthermore, epigenetic,
mutation-associated data and miRNA-mRNA interactions indicated ACADM, MYC, NDUFS2,
PDHB, ALDH9A1, SUCLG1, EHHADH, HSD17B4, and SDHB are implicated in dysregulated
cellular metabolism, oxidative stress resulting from mitochondrial beta-oxidation dysfunction,
aberrant peroxisomal activity, and the potential activation of proto-oncogenes, cell growth
signaling, prolonged survival, and tumorigenesis. Survival analysis indicates the worst overall
survival in the case of ACADM, PDHB, and SUCLG1 genes.
Conclusion: In this study, the WGCNA and PPI network analysis approach identified a total of
25 hub genes that are potentially involved in HCC development from NAFLD. Out of these, nine
hub genes were pinpointed, displaying elevated expression levels within hepatic cells.
Additionally, these genes displayed a heightened occurrence of mutations, along with epigenetic
modifications and post-transcriptional changes. Furthermore, survival analysis highlights the
potential of ACADM, PDHB, and SUCLG1 as promising candidates for prognostic biomarkers in
the context of NAFLD-mediated HCC development.
Keywords: NAFLD, NASH, HCC, WGCNA, Mutation analysis, PPI, Hub genes, Biomarkers
Indian Conference on Bioinformatics 2023 - Inbix'23
83
Genomic Investigation of the Heat Shock Transcription Factor Gene
Family Leveraging the Secrets of Drought Resistance in Black Pepper.
S. Mukesh Sankara Sona Charlesa Hibbah Na Soumya SLb Shivakumar MSa K.V. Sajia T. E
Sheejaa aICAR-Indian Institute of Spices Research Kozhikode, Kerala, India bCentre for Post
Graduate Studies and Research in Botany, St. Joseph's College (Autonomous), Devagiri,
Kozhikode, Kerala, India
sonayuv@gmail.com
Abstract
Black pepper (Piper nigrum L.; 2n = 52; Piperaceae), known as the "king of spices," is cultivated
in more than 30 tropical countries and holds global significance due to its extensive use in diets,
medicines, and preservation. The productivity of black pepper in traditionally cultivated areas has
been declining primarily due to drought stress, which is exacerbated by global warming and
climate change. Black pepper is naturally sensitive to drought, and studies reveal that the crop in
its reproductive stage requires up to 3,000 mm of water, making it highly susceptible to water
deficits that often result in plant mortality. Various mechanisms have been proposed to address
this challenge to protect plants from drought stress, including the induced systemic tolerance (IST)
process. Within this context, the HSF gene family, encoding specific chaperones, plays crucial
roles in multiple abiotic tolerance processes. In our study, we comprehensively analyzed the Hsf
gene family in black pepper through whole-genome identification and characterization, which is
otherwise poorly understood. A total of 41 Hsf genes were identified in the P. nigrum genome, and
these genes were unevenly distributed on 19 chromosomes. Detailed annotation using the
HEATSTER database for the different domains and motifs indicated 19 belong to HsfA class, 21
belong to HsfB class, and the remaining one got included in HsfC class. Afterward, the neighbor-
joining method constructed a phylogenetic tree indicating a similar clustering pattern. The Hsf
genes in the same group had similar gene and protein structures. Selection pressure analysis
indicated that duplicated genes underwent purification selection during evolution. This is the first
insight into this gene family and the results provide some gene resources for future gene cloning
and functional studies toward the improvement in stress tolerance of the crop.
Indian Conference on Bioinformatics 2023 - Inbix'23
84
Role of CTC1 and DNA Repair Proteins at the Telomere in Cancer
Apurwa Mishra and Trupti Patel
Vellore Institute of Technology, Vellore
tnpatel@vit.ac.in
Abstract
The CTC1 protein functions as a part of the CST protein complex, a heterotrimer consisting of
CTC1–STN1–TEN1 which promotes telomere DNA synthesis and is required for multiple steps
in telomere replication and synthesis of the complementary C-strand. CTC1 is an important
component in regulating proper telomere length and preserving telomeric integrity. The study aims
to learn more about the way CTC1 interacts with proteins involved in double-strand break repair
facilitating in maintaining genomic homeostasis. Interactomes were created between CTC1 and
various repair proteins. Strong interactions were found between proteins of Non-homologous End-
joining and Homologous Recombination. Non-synonymous SNPs were mined from COSMIC and
detailed mutational profiling was carried out. Out of 379 non-synonymous mutations, 2 were found
to be highly detrimental. HOPE and CONSURF were used to analyze the mutations to better
understand their effects on protein structure and evolutionary conservation. CTC1 detrimental
SNPs were docked with wild-type repair proteins using various in-silico tools to further understand
how CTC1 mutations affect gene repair. Shift in binding energy hints at the altered dynamics
between CTC1 and its interacting partners. Because genomic instability is a major driving force in
the development of cancer, aging-related diseases, and other complex diseases, these changes may
result in disrupted interactions with repair proteins and the downstream pathway, leading to the
onset of Alternative lengthening of telomere (ALT) and genomic instability, hallmarks of cancer.
Keywords: telomere, CTC1, DNA repair, cancer, in silico profiling
Indian Conference on Bioinformatics 2023 - Inbix'23
85
Mutational Profiling of PPARGC1A and Its Role in Diabetes, Obesity
and Cancer
Shrikirti Anand and Trupti Patel
Vellore Institute of Technology, Vellore
tnpatel@vit.ac.in
Abstract
Type 2 Diabetes mellitus (T2DM) and Obesity are two metabolic disorders caused by a range of
genetic and environmental factors. Currently we have many research evidences suggesting that the
Peroxisome proliferator activated receptor gamma (PPARG) gene is significantly involved in the
regulation of T2D as well as in the growth and differentiation of adipocytes. Due to this, PPARG
has gained popularity as an important therapeutic target for diabetic and obesity therapies. A
transcriptional coactivator of PPARG, the Peroxisome proliferator-activated receptor gamma
coactivator 1-alpha (PPARGC1A) plays an essential role in cellular energy metabolism. PPARCIA
acts on the nuclear receptor PPARG, thus interacting with transcription factors. PPARGC1A gene,
owing to its dual role in lipid and glucose metabolism has often been associated with cancer as
modifications and adaptations in cellular metabolism are hallmarks of cancer cells. In this study,
we attempt to establish that mutant PPARGC1A alter its interaction with DNA repair genes, thus
resulting in mutagenesis eventually leading to increased cellular proliferation and genomic
instability. Using in silico approach, interactome of PPARGC1A and various DNA repair proteins
suggested its interaction with repair proteins involved in Nucleotide Excision Repair and DNA
damage response mechanism. Detailed mutational profiling of PPARGC1A gene suggested that 2
out of 439 non-synonymous Single Nucleotide Polymorphism (nsSNPs) were detrimental for
protein function. Many of these mutations were evolutionary conserved making them more
impactful at the site of mutation. Docking of mutant ppargc1a with wild type repair proteins
showed modified dynamics which may cause long term adverse impact on genome integrity.
Further investigation on PPARGC1A regulation will help us in identifying novel mechanisms
underlying the relationship between T2D, obesity and cancer.
Keywords: Cancer, Type 2 Diabetes, Obesity, Mutational profiling, DNA repair
Indian Conference on Bioinformatics 2023 - Inbix'23
86
Establishing a Computational Screening Framework to Identify
Environmental Exposures Using Untargeted GC-HRMS
Hayoon Kim, Radnor High School
hayoonk481@gmail.com
Abstract
E-waste exposure to humans has been an issue for both developing countries. As technology has
advanced, the production of waste has increased, and the toxicity of this e-waste has been giving
workers adverse health effects. Liquid crystal monomer (LCM) is one of the toxic organic
compounds within e-waste and has been researched extensively to figure out the degree to which
it affects human health. Because this is a global concern, countries are currently trying to
implement effective solutions.
We have identified previously written research papers related to the topic of e-waste and its effect
on human health primarily by searching them in Pub Med, using specific search terms. Then, we
filtered out the non-related papers by filtering out the preprints, retracted publications, and other
animals (excluding humans). Moreover, we filtered out the papers by dividing them based on the
categories of include, exclude, and review. This way, we were able to have a list of only the related
papers for our review. After developing this list, we looked over the research papers in our list and
reviewed what has been discovered about e-waste exposure and the harm and also about what
should be done to further solve the issue.
The research papers proved the detrimental effects of e-waste on the human body. Specifically,
they proved that LCM plays a major role in being the toxic component inside e-waste. Organizing
sources based on search terms and filtering them through two different filtering methods allows
relevant research papers to be gathered efficiently. Moreover, future studies would have to reveal
further details of the e-waste management and LCM from the e-waste.
Keywords: E-waste, Electronic waste, Human health, Toxicity, Liquid crystal monomer (LCM)
Indian Conference on Bioinformatics 2023 - Inbix'23
87
Triphala-Induced Oxacillin Sensitivity of Methicillin Resistant
Staphylococcus Aureus Mu50 Strain: Insights from In Silico Studies
Sharadha S, Aathithya Diaz, Subbiah Thamotharan, and Vigneshwar Ramakrishnan
SASTRA Deemed to be University, Thanjavur
vignesh@scbt.sastra.edu
Abstract
Finding innovative strategies to prevent bacteria from developing drug resistance is crucial since
antibiotic-resistant bacteria are constantly evolving. Triphala, an ayurvedic formulation, has been
shown to have synergistic effects with antibiotics. Specifically, it has been shown that the triphala
increases the sensitivity of Methicillin Resistant Staphylococcus aureus (MRSA) to oxacillin. In
this study, we attempted to delineate the molecular mechanism by which triphala increased the
sensitivity of MRSA using a computational approach involving molecular docking of the
phyotchemicals of triphala against the proteome of the MRSA Mu50 strain. A total of 66
phyotchemicals from the fruits of Phyllanthus embilica, Terminalia belerica and Terminalia
chebula that constitute triphala were obtained from the IMPPAT database. The proteome of MRSA
Mu50 strain was obtained from the UniProt database. The proteins which had the corresponding
structure in the PDB database were identified resulting in 127 protein targets. Molecular docking
was performed against these targets using Schrödinger software suite. Docking studies revealed
that the phytochemicals 1,3,6-tri-O-galloyl-beta-D-glucose (IMPHY000783) and
trigalloylglucose (IMPHY013560) had the highest binding affinity to the proteins
dehydropantonate 2-reductase (3G17) and aldehyde dehydrogenase (3TY7), respectively. Pathway
analysis showed that these proteins are involved in pantothenate biosynthesis pathway and
aldehyde metabolism, respectively. Taken together, our results show that triphala increases
sensitivity to oxacillin by modulating proteins that are crucial to the survival of the bacteria and
not directly on the antibiotic resistance conferring proteins.
Keywords: Ayurvedic formulation triphala, Antibiotic resistance, Synergistic effect with
antibiotics, Increase sensitivity, Molecular docking, Binding affinity, Pathway analysis.
Indian Conference on Bioinformatics 2023 - Inbix'23
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In Silico Determination of Multidrug-Resistant (MDR) Genes in 2023
Sequenced Klebsiella Pneumonia’s Genome
Sandeep Kumar C
Jain (Deemed-to-be University)
drsangene18@gmail.com
Abstract
Klebsiella pneumoniae is a Gram-negative enteric bacterium that causes urinary tract and
nosocomial infections; widespread of multidrug-resistant (MDR) strains of K. pneumoniae are
reported across the globe. MDR in K. pneumoniae are becoming more common in clinical settings,
which is a serious worldwide health concern. Both extended-spectrum -lactamases (ESBL) and
carbapenemases capable of hydrolyzing newer carbapenem medications can be found in K.
pneumoniae. Resistance to other antibiotics, such as fluoroquinolones, aminoglycosides,
trimethoprim, and sulfamethoxazoles, is frequently associated with ESBL-producing K.
pneumoniae. Clinical isolates of K. pneumoniae have been shown to exhibit all three broad
mechanisms of drug resistance in Gram-negative bacteria: the acquisition of novel antibiotic
catalytic genes, mutations of antibiotic targets and membrane proteins, and differential expression
of specific genes such as those for efflux pumps that mediate drug effects.
The application of bioinformatics in the study of antimicrobial resistance in microorganisms
involved in human pathology has been enabled by the implementation of certain technologies such
as whole genome sequencing (WGS) or mass spectrometry, as well as creation of national and
international databases that include and gather data on MDRfrom around the world. In the present
study, VRprofile web server was used to identify the different MDR genes in the pan-genome of
K. pneumoniae.
Keywords: Klebsiella pneumoniae, multidrug-resistant (MDR) strains, whole genome
sequencing, antimicrobial resistance, VRprofile web server, Horizontal gene transfer
Indian Conference on Bioinformatics 2023 - Inbix'23
89
RNA-seq and sRNA-seq Analysis in Black Pepper Reveals Potential
Regulatory Transcripts in Drought Tolerance
Sona Charles, Sheeja Te, Priyanka Behera, Fayad A, Krishnamurthy Ks and Johnson K George,
ICAR-Indian Institute of Spices Research, Kozhikode
sonayuv@gmail.com
Abstract
Black pepper (Piper nigrum), also known as “King of Spices” is widely known for the panoply of
metabolites with potential medicinal and biological properties. Transcriptome-wide studies in
black pepper can uncover key genes, small RNAs and pathways that contribute to its stress
tolerance. In this study, rooted cuttings of black pepper (genotype: IC 317179) were grown under
normal as well as water stressed (15 days stress by withholding water) conditions. RNA was
isolated from the leaves of both normal and water stressed plants and Illumina HiSeq 2000
platform was used for the paired-end sequencing. Raw reads of RNA as well as sRNA were pre-
processed using FASTQC. De-novo and reference-based assembly of drought stressed
transcriptome were performed. Thirty-one differentially expressed miRNAs (log fold change |>1|
and p-value<0.05) were filtered. Identified miRNAs depicted stable stem-loop structures and high
sequence conservation among other plant species such as Arabidopsis. The minimum free energy
index of the sequences ranged between 0.63 to 0.80 and AU composition of pre-miRNA ranged
between 32% to 64%. Putative target transcripts of miRNAs were also predicted. The differentially
expressed miRNA were found to target genes specific for stress response. Functional annotation
of the targets revealed that the miRNAs regulate drought responsive genes such as ribosomal
protein S27a, catalase isozyme 1, etc. The target genes were mainly associated with stress and
were found to be involved in pathways related to carbohydrate metabolism, translation and
ribosome biogenesis. The findings of the study provide new insights into miRNA mediated
regulatory networks of drought response in black pepper. The insights gained from transcriptome
analysis will be validated to uncover key interactions between miRNAs and genes in pathways
that contribute to its stress tolerance.
Keywords: drought, Stress, miRNA, black pepper, transcriptome
Indian Conference on Bioinformatics 2023 - Inbix'23
90
Design of a Multi-Epitope Based Vaccine using Spike Glycoprotein for
Effective Protection Against COVID-19 through Bioinformatics
Approach
Puja Jaishwal and Satarudra Prakash Singh
Mahatma Gandhi Central University, Motihari, Bihar
sprakashsingh@mgcub.ac.in
Abstract
The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the fundamental
agent of coronavirus disease 2019 (COVID-19), which has been spread worldwide since it was
first identified in Wuhan, China, at the end of 2019. With the global transmission of the virus,
many SARS-CoV-2 variants have a high rate of mutations which affect the epitope conservancy
and create obstacles in vaccine design. Conserved epitopes are the desired target in peptide-based
vaccine design to enhance the efficacy of vaccine. This study aims to establish an efficient multi-
epitope vaccine construct that could elicit both T-cell and B-cell responses and neutralize the
SARS-CoV-2 virus. In present work, B- and T-cell epitopes of spike glycoprotein were extracted
from IEDB database, and the suitable epitopes were systematically screened using crucial
parameters of vaccine effectiveness through immunoinformatic tools involving the assessment of
the HLA class I and II binding efficiency, population coverage, along with conservancy among
SARS-CoV-2 variants of concern. Results of the screening finally identified 3 overlapping B-cell
and T-helper cell epitopes, 1 unique T-helper cell epitopes and 8 unique cytotoxic T-cell epitopes.
These epitopes were used in the final vaccine construct design through appropriate linkers and
showed 99.45% world population coverage. In future, a tertiary structure of the designed vaccine
construct will be developed along with the suitable adjuvant and its interaction with toll-like
receptors (TLRs) as well as stability will be evaluated through molecular modelling and simulation
studies.
Keywords: COVID-19, Vaccine, Bioinformatics, Modelling, MD Simulation
Indian Conference on Bioinformatics 2023 - Inbix'23
91
Gut Metagenomic Analysis of Gastric Cancer Patients Reveals
Akkermansia, Gammaproteobacteria, Veillonella Microbiota as
Potential Non-invasive Biomarkers
Anju R Nath and Jeyakumar Natarajan
Data mining and text mining lab, Department of bioinformatics, Bharathiar university
anjuraas@gmail.com
Abstract
The goal of the study was to investigate the changes in the gut microbiota during the advancement
of gastric cancer (GC) and identify pertinent taxa associated with the disease. We used a public
fecal amplicon gastric cancer dataset from the Sequence Retrieval Archive (SRA), of patients with
GC, gastritis, and healthy individuals. We did sequence pre-processing, including quality filtering
of the sequences. Then, we performed a diversity analysis, evaluating α and β-diversity. Next,
taxonomic composition analysis was performed and the relative abundances of different taxa at
the phylum and genus levels were compared between GC, gastritis, and healthy controls. The
obtained results were subsequently subjected to statistical validation. To conclude, metagenomic
function prediction was carried out, followed by correlation analysis between the microbiota and
KEGG pathways. α analysis revealed a significant difference between male and female categories,
while β analysis demonstrated significant distinctions between GC, gastritis, and healthy controls,
as well as between sexes within the GC and gastritis groups. The statistically confirmed taxonomic
composition analysis highlighted the presence of the microbes Bacteroides and Veillonella.
Furthermore, through metagenomic prediction analysis and correlation analysis with pathways,
three taxa, namely Akkermansia, Gammaproteobacteria, and Veillonella, were identified as
potential biomarkers for GC. Additionally, this study reports, for the first time, the presence of two
bacteria, Desulfobacteriota and Synergistota, in GC, necessitating further investigation. Overall,
this research sheds light on the potential involvement of gut microbiota in GC pathophysiology;
however, additional studies are warranted to explore its functional significance.
Keywords: Gastric Cancer, Metagenomic Analysis, Gut Microbiota, Diversity Analysis, QIIME2
Indian Conference on Bioinformatics 2023 - Inbix'23
92
In Silico Predictive Homology Modeling of PKHD-1 Protein: A
Comparative Study among Three Different Species
Arunannamalai S
St. Joseph's College of Engineering
arun03bt@gmail.com
Abstract
PKHD-1 (Polycystic Kidney and Hepatic Disease-1) gene encodes a vital protein critical for renal
and hepatic functions. Mutations in PKHD-1 lead to a severe type of disorder in early infancy
called Autosomal Recessive Polycystic Kidney Disease (ARPKD). The PKHD-1 protein structure
remains unavailable in databases such as PDB, with only a few low-resolution structures accessible
in the Swiss Model Template Library. Therefore, Homology Modeling was employed to generate
structural models of PKHD-1 proteins derived from three different species [Homo sapiens
(Human), Mus musculus (Mouse), Canis lupus familiaris (Dog)]. The mouse PKHD-1 protein was
structurally predicted by employing the AlphaFold DB model based on the PKHD1 ciliary IPT
domain of fibrocystin/polyductin from Rattus norvegicus as a reference template. Additionally,
the human and dog PKHD-1 proteins were modeled using the AlphaFold DB model of the G8
domain-containing protein from Marmota monax as the template for the prediction process. In
addition, we employ GOR4 for analyzing secondary structure, ProtParam for assessing
physicochemical properties, QMEAN for evaluating the quality of protein structure, and
MolProbity for validating protein structures along with obtaining the Ramachandran plot. The
binding pockets were also predicted using P2Rank tool (PrankWeb web server).
Keywords: PKHD-1 protein, Autosomal Recessive Polycystic Kidney Disease, In silico
analysis, Bioinformatic tools, Homology Modeling
Indian Conference on Bioinformatics 2023 - Inbix'23
93
Genetic Variations and their Impact on Diabetic Retinopathy
Pathogenesis: A Genomic Evolution Perspective
Shafna Asmy V S and Jeyakumar Natarajan
Datamining and text mining lab,Dept of bioinformatics,Bharathiar university
shafnaasmy.v.s@gmail.com
Abstract
Diabetic retinopathy (DR) is an eye disorder that can cause vision loss and eventually blindness in
people who have had diabetes for more than a decade. This study investigated the history of genetic
changes within the human genome, tracing the path from normal retinal tissue through diabetic
retinal tissue and, eventually, to the formation of DR. We investigated three types of genetic
variants to accomplish namely SNPs (single nucleotide polymorphisms), SSRs (simple sequence
repeats), and InDels (insertions and deletions). These genetic changes were systematically studied
in samples representing normal, diabetic, and DR-affected retinal tissue. SNP-influenced genes
were discovered and segregated from the rest of the genetic material after a thorough study of these
changes. Furthermore, the analysis identified genes with different levels of expression among the
SNP-affected genes. These genes were thoroughly investigated in order to acquire insight into the
evolution of SNPs and SSRs. SNPs were found in all three samples' unigenes: normal (19,97,007),
diabetes (6,16,892), and DR-affected retinal tissue (11,43,374). The study resulted in five
important genes—SPDYA, CACNA1C, NXF2, DNAH12, and LOC105378947—that they have a
substantial role in the development of DR. This determination was made through a meticulous
analysis of SNPs and InDels, complemented by studies on differential gene expression.
Keywords: SNP Analysis, Evolution, NGS, SSR analysis, Diabetes
Indian Conference on Bioinformatics 2023 - Inbix'23
94
Machine learning for T-cell epitope prediction
Riddhi Wankhade, University of Pune
riddhi_mehta74@yahoo.com
Abstract
Precise delineation of lymphocyte antigenic determinants is crucial for advancing therapeutic
strategies in the domain of immunomodulation. Computational prediction is a lot cheaper than
experimental validation for T cell epitope prediction. Researchers have previously predicted T cell
epitopes in both a pan-specific and allele-specific fashion with varying degrees of success. In this
manuscript, I leverage computational methods, specifically Kernel-based classifiers, to anticipate
antigenic regions recognized by T-cell receptors. My findings are compared to the leading method,
NetMHCII. While my results closely align with those obtained by established techniques, it is
apparent that the robustness of my predictions relies significantly on the input data. This
underscores the imperative for additional research endeavours to position my approach
competitively in novel predictive applications.
Keywords: Machine learning, Major Histocompatibility Complex, Net MHC- II, Polymorphism,
PREDIVAC, AUC, Margin Based classifiers, Interspersed, Orthogonal Vector, Skewed, Multi
RTA, Quadratic Programming
Indian Conference on Bioinformatics 2023 - Inbix'23
95
Bioinformatics for Drug Discovery Against Drug Resistant Candida
Species
Akshay Kisan Mundhe, Premanand Adaikalasamy and Reena Rajkumari Baskaran
School of Bio Sciences and Technology, Vellore Institute of Technology
b.reenarajkumari@vit.ac.in
Abstract
Infectious diseases caused by fungi contribute significantly to global mortality rates. Among the
leading causes of invasive fungal infections are various Candida species, with Candida albicans
being the primary causative agent in invasive candidiasis. Developing effective antifungal drugs
is challenging due to the limited availability of distinct biochemical targets shared between fungi
and their human hosts. Currently, only three primary drug classes are approved for treating
Candida infections, and the emergence of drug resistance poses a substantial threat to their
efficacy. In this study, we have selected CLB2 as a target to inhibit Candida albicans growth based
on literature. We performed molecular docking of this target with nearly fifteen thousand ligand
molecules using AutoDock Vina software. Molecular docking results were subsequently subjected
to molecular dynamic simulations. From this extensive screening, we identified 17 out of 14,965
ligands that formed covalent bonds with active site residues of the selected target molecule. Further
in-vitro experimental studies are being conducted to test the inhibitory potential of the selected
compounds against wild and drug resistant Candida albicans strains.
Keywords: pathogenic fungi, Candida albicans, candidiasis, drug resistance, in-silico drug
screening, virtual screening, molecular docking, structure-based drug discovery, herbal medicine
Indian Conference on Bioinformatics 2023 - Inbix'23
96
3’UTR SNP rs4709267 Associated with Rheumatoid Arthritis Risk and
Enhances TAGAP Gene Expression
Shri Preethi M1, Balaji Nandagopal2, Raja N3, Raja Sudhakaran1, Anbalagan M1, Karthikeyan
K1 and Asha Devi S1
Vellore Institute of Technology1,
Sri Narayani Hospital and Research Center, Vellore2,
Sri Padmavathy Rheumatic Care Center, Vellore3
ashaselvaraj74@gmail.com
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease. Single-nucleotide polymorphisms
(SNPs) in T-cell activation Rho GTPase activating protein (TAGAP) gene is associated with
rheumatoid arthritis (RA). In the present study, High-Resolution Melting Analysis (HRMA)
technique was employed for SNP genotyping, and the frequency of the genotype association with
RA was statistically correlated. To investigate the impact of SNP rs4709267 on the TAGAP gene
expression level, TAGAP 3´UTR sequences with SNP rs4709267 and wild type were cloned into
the pGL3 SV40 vector. The cloned vectors were transfected to HeLa cells and luciferase
expression were compared and internal control pRL_SV40 vector was used. Significant increase
(p<0.05) in the expression of luciferase activity, suggests that the SNP rs4709267 might be
associated with RA and affect the TAGAP gene expression level.
Introduction: Several genetic variants linked to RA susceptibility have been discovered in studies
examining the relationship between polymorphisms in human genome sequences and RA case-
control characteristics.
Background: A polymorphism causes many autoimmune disorders in the TAGAP gene, namely,
rheumatoid arthritis, Crohn's disease, celiac disease, and multiple sclerosis.
Materials and method:
SNP genotyping
The study received hospital ethical clearance and the genotyping of rs4709267 (A/G) at 3’UTR of
the TAGAP gene was accomplished employing High-Resolution Melting Analysis (HRMA).
Cloning of the TAGAP 3 UTR sequences in pGL3 -SV40 vector
Indian Conference on Bioinformatics 2023 - Inbix'23
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The wild and polymorphic variants of the 3'UTR region (rs4709267 A/G allele) of the TAGAP
gene were obtained. The luciferase assay was performed according to the kit’s protocol (Dual-
Glo® Assay system Promega, USA).
Results:
SNP genotyping using HRMA
The frequency of the A and G alleles among the controls were 0.87(87%) and 0.13(13%),
respectively, whereas, among RA patients, it was 0.6 (60%) for the A allele and 0.4 (40%) for the
G allele. The statistical analysis proved that the odds ratio of AG (2.5926) and GG (1.0532)
genotype is greater than the odds ratio of AA (0.1012).
Impact of rs4709267 on luciferase gene expression
A significant (p<0.05) increase in luciferase activity was observed in 3’UTR_ G allele construct
than in the 3’UTR_A allele construct (figure 1).
Figure 1: depicts the pGL3-SV40/pRL-SV40 relative ratio obtained after vector normalization.
ANOVA analysis: p value <0.05
Conclusion: The SNP rs4709267 G allele frequency was higher among RA patients than control
by the HRMA technique. The SNP rs4709267 3’UTR G construct showed a significant increase
in the luciferase activity than 3’UTR with A allele, thereby showing that the SNP rs4709267 (A/G)
affects the TAGAP gene.
Keywords: Rheumatoid arthritis, TAGAP, HRMA, Luciferase assay, pGL3 SV40, SNP
Indian Conference on Bioinformatics 2023 - Inbix'23
98
Studies on microRNAs and Their Target Genes Through Expression
Analysis in Breast Cancer
Saumya Tyagi1, Vinamrata Sharma1, Vikram Singh2 and Tiratha Raj Singh1
Jaypee University of Information Technology1
Central University of Himachal Pradesh2
vikramsingh.jnu@gmail.com
Abstract
Rationale/Motivation: To elucidate the key genes and miRNAs related to breast cancer and their
interactions with each other and with other molecular or cellular processes. Breast cancer is the
most common cancer among women worldwide. It is a complex and heterogeneous disease,
characterised by a diverse range of genetic and molecular alterations. Understanding which is
essential for developing more effective diagnostic and therapeutic strategies. One of the recently
recognized factors to breast cancer-causing pathways is microRNA (miRNA). These are small
non-coding RNAs that play important roles in regulating gene expression at the post-
transcriptional level. microRNAs are dysregulated in breast cancer, with some miRNAs being
overexpressed and others underexpressed. This dysregulation can lead to changes in the expression
of genes that are involved in a variety of cellular processes, including cell proliferation, apoptosis,
and migration. miRNAs have been shown to play a role in all stages of breast cancer development,
from initiation to progression and metastasis. For example, some miRNAs promote tumor growth
by inhibiting apoptosis or promoting cell proliferation. Other miRNAs promote metastasis by
increasing cell migration and invasion. It is essential to understand the role of miRNAs in breast
cancer in order to develop more effective strategies for the diagnosis and treatment of this disease.
This includes identifying the miRNAs that are dysregulated in breast cancer and the genes that
they target. It is also important to understand how miRNAs interact in order to unravel the
mechanisms behind the development of breast cancer.
Objective: To identify target genes and microRNAs involved in breast cancer (BC), as well as
those that are often overexpressed and underexpressed, and to understand the regulatory network
between these genes. This study performed a bioinformatic analysis of miRNA-BC studies using
a variety of datasets. This study focuses on identifying and analysing differentially expressed genes
that are common across different age groups, one from young women (age ≤ 40 years) and one
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from older women (age ≥ 40 years). This study also explores the regulatory networks of the genes
which regulate breast cancer, with a focus on important genes that interact with each other in breast
cancer development and progression. Additionally, we will carefully study the gene ontology of
each gene we identify, giving us a comprehensive understanding of how they work in the context
of breast cancer.
Material and Methods: In this study, we used bioinformatics analysis to identify common
overexpressed and underexpressed genes and miRNAs associated with breast cancer. We analysed
gene expression data from the GEO Dataset platform using GEO2R to identify differentially
expressed genes that are common across different age groups. We also explored the regulatory
networks and gene ontology of the identified genes and miRNAs using the KEGG and STRING
databases.
Data retrieval: We retrieved gene expression data from the GEO Database, miRBase, TargetScan,
GeneCards. Differential expression analysis: We used GEO2R to identify differentially expressed
genes that are common across different age groups in the breast cancer datasets. Gene ontology
analysis: We used the KEGG and STRING databases to explore the regulatory networks and gene
ontology of the identified genes and miRNAs.
Results and Discussion: In this extensive study, we used bioinformatics analysis to identify
common overexpressed and underexpressed genes and miRNAs associated with breast cancer in
young women (age ≤ 40 years) and older women (age ≥ 40 years). We found a number of
overexpressed genes that are common across both age groups. These findings suggest that these
genes may play a key role in the development and progression of breast cancer, regardless of age.
We also explored the regulatory networks and gene ontology of the identified genes and miRNAs
using the KEGG and STRING databases. These findings provide new insights into the molecular
mechanisms underlying breast cancer in young and older women. Additionally, the overexpressed
genes that we identified may be potential targets for new diagnostic and therapeutic approaches.
Conclusions: Our study identified common overexpressed genes and miRNAs associated with
breast cancer in young and older women, providing new insights into the molecular mechanisms
underlying the disease. These genes and miRNAs may play a role in gene regulation and other
cellular processes essential for breast cancer development and progression. This information could
be used to develop new targeted therapies and diagnostic tools for breast cancer. Therefore, our
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study provides a basis for the development of new and more effective strategies for the prevention,
diagnosis, and treatment of breast cancer.
Keywords: Breast Cancer, microRNAs, Pathways, Expression analysis, Bioinformatics.
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Nephroprotective Role of Syzygium Cumini ZnO Nanoparticles in
Streptozotocin‐Induced Diabetic Nephropathy Rats
Arul Daniel John1, Ragavee Ambalavanan2, Asha Devi Selvaraj3
University of Nebraska Medical Center1
Vellore Institute of Technology2
sashadevi@vit.ac.in
Abstract
Diabetic nephropathy (DN) is one of the major microvascular complications of diabetes and the
cause of end-stage of renal failure. Stern control of glucose levels and blood pressure are the
therapeutic regime to be followed for the prevention and progression of DN. Previous studies have
elucidated the involvement of Advanced Glycation End products (AGEs) and Transforming
Growth Factor Beta (TGF-β) in DN. In this study, we evaluated the effect of green synthesized
zinc oxide nanoparticles (ZnO NPs) on the expression of receptor for AGEs (RAGE), TGF-β and
its subsequent involvement in the treatment of DN in Wistar rats. DN was induced using
streptozotocin, which was then treated with green synthesized ZnO NPs and later checked for its
physiological and pathological changes. Treatment of DN rats with green synthesized ZnO NPs
significantly reduced (p<0.05) the blood glucose level, serum creatinine, Blood urea nitrogen
(BUN), urine protein and Urine albumin excretion rate (UAER). ZnO NPs also reduced the mRNA
levels of RAGE and TGF- β in kidney tissue, which was correlated with pathological
improvements such as reduced mesangial expansion and interstitial inflammatory cell infiltrates.
The results obtained imply that administration of green synthesized ZnO NPs improved kidney
function in DN rats by regulating RAGE and TGF- β in the kidney.
Keywords: Diabetic nephropathy; Zinc oxide nanoparticles; TGF-β; Syzygium cumin; serum
creatinine
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Identification of Potential Phytochemicals as Inhibitors of CXCR2 and
CXCR4 in Glioblastoma using Molecular Docking and Molecular
Dynamic Simulation Studies
Inayah Suheb, Shubhada Naik, Premanand Adaikalasamy and B. Reena Rajkumari
Vellore Institute of Technology, Vellore
b.reenarajkumari@vit.ac.in
Abstract
Glioblastoma represents nearly 45% of all primary CNS tumors. Its aggressive nature often makes
conventional treatments such as radiation, chemotherapy, and surgery ineffective. The variability
in patient responses during clinical trials for glioblastoma can be attributed to factors like tumor
heterogeneity, immunological resistance, the presence of the blood-brain barrier, and glioma stem
cells in the tumor microenvironment. Research indicates that human proteins, namely CXCR4 and
CXCR2, play significant roles in processes like angiogenesis, inflammation, and metastasis.
Inhibiting these proteins has been shown to suppress oncogenesis. This study utilized an in-silico
approach to screen 14,962 phytochemicals from Indian plants, aiming to identify natural inhibitors
of CXCR4 and CXCR2. The ligands were docked against the target protein using AutoDock Vina.
Based on the lowest binding energy and the number of hydrogen bonds formed between the ligands
and their target in the docked complex, the top ligands were selected for further molecular dynamic
simulation studies. Molecular Dynamic Simulation of these protein-ligand complexes was
performed using Gromacs 2022.6 to assess the protein-ligand complex stability. From these
findings, we chose five ligands that showed promising results to be investigated in in-vitro
experiments. The intent of this research is to identify potential phytochemical inhibitors that can
be used against the target proteins for glioblastoma treatment which has to be further validated
through pre-clinical studies.
Keywords: Glioblastoma, CXCR4, CXCR2, Phytochemicals, Ligands, Molecular Docking,
Molecular Dynamic Simulations
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Docking and Molecular Dynamics Simulation Revealed the Potential
Anti-Oncogenic Activity of Sesamolin in Breast Cancer Therapeutics
Targeting the E2F8, A Cell-Surface Receptor Protein
Satarupa Banerjee and Sohini Chakraborty
Vellore Institute of Technology
satarupa.banerjee@vit.ac.in
Abstract
Cell-surface proteins (CSPs) have been employed extensively in cancer research as diagnostic and
prognostic markers as well as targets for the creation of anticancer drugs. Few attempts have been
made so far to describe the surfaceome of breast cancer (BC) patients. For enabling effective BC
therapy, the identification of novel druggable biomarkers is an earnest need. Publicly available
databases are utilised to identify CSPs associated with BC. We also foresee significantly altered
receptor-ligand interactions in cancer, and we pinpoint significant CSPs and druggable
polyphenols (DPs) with therapeutic potential for the disease using systems biology methods.
Modern computer-aided drug designing techniques, thus aim to design a cost-effective DP, a
natural agent for BC prevention and diagnosis. Here, 56 polyphenols with druggable properties,
are initially docked with 3172 CSPs. Finally, duplicate docking was done for the five DPs against
the nine significant CSPs identified in BC and proceeded for simulation. The preliminary result of
the analysis, reports the highest binding energy scores of E2F8-Sesamolin to be the best-docked
protein-ligand complex with a binding energy of -12.22kcal/mol which was then simulated and
compared with an approved drug for BC treatment, Olaparib. A comparable binding energy score
of -10.62kcal/mol was obtained by docking Olaparib with E2F8. A 100 ns MD simulation revealed
that Sesamolin formed more H-bonds (1 to 5), providing a more stable and compact protein-ligand
complex with E2F8 as compared to Olaparib (1 to 2). The result was also supported by solvent-
accessible surface area, radius of gyration alongside MM-PBSA interaction energies of ΔG values
of -51.160+/-18.054 KJ/mol (-12.22 kcal/mol) for Sesamolin which is much better as compared to
-44.441+/-18.127 KJ/mol (-10.62 kcal/mol) for the approved drug, Olaparib. Expression,
oncoprint, survival and functional enrichment profiles of the significant CSPs are also analysed in
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the study. Thus, our findings suggest that the role of Sesamolin can further be studied in detail for
BC therapeutics, which was found to target the E2F8, a CSP receptor in a stable manner.
Keywords: Breast cancer (BC), Cell surface proteins (SPs), druggable polyphenols (DPs),
sesamin compounds, binding energies, therapeutics
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An In Silico Study on Analysis of the Interaction Between Microplastics
and Aquatic Pathogens
Amrutha V.M and Sudhakaran Raja, Vellore Institute of Technology
sudhakaran.r@vit.ac.in
Abstract
Plastic plays a vital role in all sectors they are durable, cost-effective, and easily carriable. Due to
various aspects such as UV exposure, climatic changes, oxidation, and mechanical stress; plastics
get shredded into small fragments. Plastics less than 5mm are considered to be microplastics some
commercially available microplastics were manufactured for aircraft, cosmetics, and microfibers
for clothing. These microplastics have a great impact on both marine and terrestrial environments.
There are many scientific reports that indicate the presence of microplastics in marine organisms
is so threatening and tropic transfer of microplastics is also noticed. There are many possible ways
shrimp may be contaminated with viral and bacterial diseases due to the interaction of
microplastics and pathogens. White spot syndrome virus (WSSV) VP28 is a viral infection of
penaeid shrimp that causes 100% mortality in 3-5 days in commercially available farms. Vibrio
parahaemolyticus is a gram-negative bacterium, rigid and comma-shaped, and mass mortality is
seen in Vibrio species-affected shrimp. This study implies microplastics may act as a vector for
marine pathogens so the interaction between microplastics such as polyamide, polyethylene,
polypropylene, polyvinyl alcohol, and polyethylene terephthalate was docked with the target
protein, stimulation carried out and further in vivo studies will be performed for confirmation
Keywords: Microplastics, Docking, WSSV, Vibrio parahaemolyticus
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A Novel Cuproptosis related miRNAs to predict the prognosis of Renal
Cell Carcinoma
Subeka Abraham Gnanadass and Pragasam Viswanathan
Vellore Institute of Technology
pragasam.v@vit.ac.in
Abstract
Background: Renal Cell Carcinoma (RCC) is one of the most devastating genitourinary cancers
worldwide. The treatment strategies for tackling RCC could be better as the recurrence rate is high,
and they need more tools for early detection. Further, the prognosis of RCC still needs
improvement. Thus, new strategies must be explored to predict the prognosis of RCC. All
organisms require copper as a cofactor to sustain life activities since it is essential to several
biological processes, including iron intake, antioxidant/detoxification, and mitochondrial
respiration. Cuproptosis is a unique form of copper and proteotoxic-induced cell death, closely
associated with the TCA cycle and mitochondrial respiration. Recent research states that copper
accumulation is highly associated with cancer progression and development. The regulatory role
of cuproptosis in RCC is still a mystery.Micro-RNAs are tiny non-coding RNAs that regulate gene
expression transcriptionally and translationally. They are key players in biological processes like
cell differentiation, cell death, and cell proliferation. Dysregulation of the miRNAs is highly
related to the hallmarks of cancer.
Motivation: This research aims to explore the regulatory role of cuproptosis-related genes and
miRNAs in RCC development and progression.
Methods: The miRNA and mRNA transcriptomic data were obtained from the TCGA database.
The miRNAs related to the cuproptosis gene signatures were identified by the TargetScan
database. The differentially expressed R package identified cuproptosis-related genes and
miRNAs. Further, their functional enrichment analysis was done by David and KEGG pathway
analysis. The Cuproptosis-related miRNA gene signatures' survival analysis has been constructed
using Kaplein-Meier analysis.
Results: An extensive literature survey identified the mRNAs associated with the Cuproptosis
process. Out of 29 cuproptosis-related genes, six genes (FDX1, CDKN2A, DBT, NLRP3, PDHA1,
and PDHB) were differentially expressed in RCC transcriptomic datasets (logFC >1 and p-value
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< 0.05). The differentially expressed miRNAs related to cuproptosis are miR-215-5p and miR-
223-3p. The functional enrichment analysis revealed that they play a potential role in cancer
development and progression. The identified miRNAs have great prognostic and diagnostic value.
Conclusion: Thus, the constructed novel Cuproptosis-related miRNA gene signature predicts the
prognosis of RCC, and this might pave the way for new treatment strategies in RCC.
Keywords: Renal Cell Carcinoma, miRNA, Cuproptosis, Cancer therapy, Non-coding RNAs
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Immunoinformatic-based Vaccine Candidate Development for
Edwardsiellosis
Sikhakolli Yeswanth, Aadil Ahmed, Thomas Theodore and Anand Prem Rajan
Vellore Institute of Technology, Vellore
janandpremrajan@vit.ac.in
Abstract
Aquaculture is one of the fastest-growing food production industries which provides income to a
lot of people, but some fish pathogens have become an adversary to the production and are causing
a lot of economic loss to the fisheries industry. One such pathogen causing the most prevalent
disease of Edwardsiellosis is Edwardsiella tarda a facultative anaerobic bacterium. Throughout the
period these bacteria got resistant to so many antibiotics which makes it difficult to treat this
disease hence vaccination is the best way to prevent this disease. This study has been designed to
predict in-silico peptide vaccine candidates by finding out the T-cell epitopes present on the
genes(TolC, OmpA, TamA,TamB) of Outer membrane protein sequence(OMPs) and their binding
interaction with MHC class I alleles using in-silico immuno-informatics which uses
Bioinformatics tools & servers like Kolaskar and Tongaonkar antigenicity tool, NetCTL.1.2
server, Galaxy-pep dock for docking & modelling of protein-peptide structures, Flex-pep for
refining those modelled structures, chimera & pymol for interaction. These tools are freely
available and approved by CAPRI (Critical Assessment of Predicted Interactions). Such
interaction between peptides and MHC class I alleles helped in finding out the potential peptide
vaccine candidates. Finally, it’s found that two, six & five, four epitopes from TolC, OmpA and
TamA, TamB genes respectively have good interaction with MHC class I alleles. The present study
can be validated by performing in-vitro studies which help in the development of potential vaccine
candidates for Edwardsiellosis.
Keywords: edwardsiellosis, T-cell epitopes, peptide vaccine, OMP
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Comparative transcriptome analysis reveals insights into the growth
traits of Penaeus monodon
Preety Sweta Hembrom, Mottakunja Deepthi and Tony Grace
Central University of Kerala
tonygrace@cukerala.ac.in
Abstract
The black tiger shrimp, Penaeus monodon, holds significant commercial importance in the marine
industry, contributing to about 9% of the total crustacean production and being extensively farmed
for human consumption. During the culture of Penaeus monodon, there tend to be notable
differences in growth within the same family under the same food, water quality, and environment,
which has a significant impact on cultivation efficiency. To explore the molecular mechanism of
this growth difference, this study used RNA-seq technology to compare the transcriptomes of P.
monodon individuals with significant growth differences from the same family. A total of over 74
million and 72 million paired-end reads were generated from large and small P. monodon samples
respectively. Furthermore, we annotated 16,767 putative protein sequences derived from the
assembled transcripts, obtaining 15,305 BLAST hits. Additionally, differential gene expression
analysis revealed a diverse array of transcript sequences showing similarity to genes associated
with growth and development in large P. monodon. Some of these genes, like cuticle protein
AMP4, tubulin alpha-1 chain, and myosin heavy chain, may be linked to muscle growth. These
genes were classified into 31 KEGG pathways, with the top pathways including carbohydrate
metabolism, energy metabolism, and amino acid metabolism. Our findings contribute to a clearer
understanding of the genes involved in the molecular mechanisms governing growth traits in black
tiger shrimp, offering valuable insights for future research in shrimp development.
Keywords: Penaeus monodon, Growth traits, High-throughput sequencing, Transcriptome
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Prediction of Antimicrobial Resistance Profiles from Salmonella
Variants using ML and DL Approaches
Gopichand Boinapalli , Athulya V Varma, Anupama Anil, Kiran Manoharan, Joshy Alphonse
and Nidheesh Melethadathil
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala
ammasnidhi@gmail.com
Abstract
Motivation: Antimicrobial resistance (AMR) is one of the most critical global health challenges
of this century. AMR occurs when an organism adapts itself to the antibiotics to the point where
the medicines are no longer effective. Antimicrobial Susceptibility Testing (AST) is employed to
determine the level of resistance demonstrated by microorganisms against antimicrobial agents.
Traditional AST can be time-consuming with low-throughput and only feasible for bacteria that
can be grown. Machine learning techniques could pave the path for automated AMR prediction
based on bacteria genomic data.
Objectives: Developing ML and DL algorithms to predict antimicrobial resistance phenotypic
profiles of Salmonella variants.
Testing and validating the models specific to particular antibiotics and salmonella variants.
Methods and materials: In this study, we mainly focus on the salmonella variants which includes
Salmonella Typhi and Salmonella Non-Typhi as they showed high rate of AMR, multiple drug
class resistance and vast data availability when compared to other species. Data driven approaches
such as Artificial intelligence (AI) and machine learning (ML) methods can be utilized to predict
whether a particular variant sample exhibits resistance to a specific antibiotic drug or a drug class.
Since most of the previous have employed Whole Genome Sequencing (WGS) data for predicting
resistance profiles of Salmonella isolates, WGS data for both salmonella typhi and non-typhi
variants has been collected. This includes 5000+ salmonella non-typhi samples, and 133
salmonella typhi samples. The AMR genes of salmonella typhi and salmonella non-typhi were
annotated using BacAnt tool. This analysis revealed that both the variants contain common AMR
specific genes, indicating common resistance mechanisms in both variants. In addition, this
suggests that there is a potential likelihood that both variants share resistance to the same category
of drugs. Consequently, a subset of 250 salmonella non-typhi isolates were taken as training set,
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and complete set of 133 isolates from salmonella typhi were used as external testing set. Next,
XGBoost, SVM and Feed Forward neural network models have been trained using the train set
and were evaluated using salmonella typhi testing set.
Results and discussion: The Algorithms developed in this study include SVM, XGBoost, Feed-
Forward Network. The algorithms were trained with the k-mer sequences obtained from of
salmonella variant Whole genome sequence data, and antimicrobial resistance labels specific to
antibiotics. These models were trained on salmonella non-typhi data and validated on salmonella
typhi variant data. These models were able to achieve high accuracy rates when tested using
salmonella non-typhi variants for Ampicillin, Tetracycline, and Ciprofloxacin antibiotics. Out of
the three models that were developed in this study, ML models XGBoost and SVM demonstrated
acceptable performance, but Feed forward neural network has outperformed both the ML models
with an accuracy of 82%, 85%, 92% for Ampicillin, Tetracycline, and Ciprofloxacin respectively.
These results suggest that deep neural network architectures were more effective in capturing
relevant features for the classification task, making it the preferred choice for reliable predictions
in this study.
Conclusion: From our study it's clear that deep neural network architectures can effectively handle
intricate and non-linear data from Whole Genome Sequencing. Further this study can be expanded
to utilise all the 5000+ samples of salmonella non typhi to predict AMR profiles which can also
pave a potential opportunity for cross-species learning in addition to inter-species transfer learning.
Keywords: Antimicrobial resistance, Whole Genome Sequence, Salmonella Typhi, Machine
Learning, Deep Learning
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Validation of Sequences from Protease Producing Bacteria with
Unknown Sequences
Anchita Prasad and Vinod K Nigam
Birla Institute of Technology, Mesra, Ranchi, Jharkhand,
anchita204prasad@gmail.com
Abstract
During annotation of hypothetical proteins in a few of the bacteria like Bacillus subtilis, Bacillus
anthracis, Streptomyces coelicolor, Mycobacterium tuberculosis, Neisseria meningitidis, and
Streptococcus, we identified domains with proteolytic in nature and therefore, protease activity
was performed from the different bacteria such as Bacillus species, Halobacillus dabanensis,
Halobacillus profundi, Halobacillus trueperi and tried to annotate known 16SRNA sequences
from them to examine the functional domains. These sequences were translated first into amino
acid sequences followed by annotation using different computational tools (CDD, SMART,
Scanprosite and Superfamily). The results did not offer presence of domains in amino acid
sequences of known protease producing bacteria. We considered both top hit blasts with about
90% similarity as well as lowest similarity of 50% during domain analysis. The results of finding
will be presented at the time of conference.
Keywords: Annotation, CDD, Functional Domain, Hypothetical, SMART, Scan prosite,
Superfamily
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Drug Repurposing for Non-Alcoholic and Alcoholic Fatty Liver Diseases
based on Omics Data
Dharshana R, Vishnu Varadhan S and Dr. Selvi Subramanian
Department of Biotechnology, PSG College of Technology, Coimbatore
selvi.bio@psgtech.ac.in
Abstract
Rationale: Non-Alcoholic Fatty Liver Disease (NAFLD) can lead to liver cirrhosis and
hepatocellular cancer, as well as death from liver disease. The primary treatment for
NAFLD/NASH is currently lifestyle adjustment through diet and exercise. However,
pharmaceutical therapy is required since obese people with NAFLD frequently struggle to
maintain healthy lifestyles. The pathogenesis of NAFLD/NASH is not well understood. However,
insulin resistance, inflammatory cytokines, and oxidative stress are thought to play a role in the
disease's genesis and/or progression. The analysis of Genome-Wide Association Studies (GWAS),
Phenome-Wide Association Studies (PheWAS) and Transcriptome data for drug repurposing from
Alcoholic Fatty Liver Disease (AFLD) to NAFLD is a rational and efficient strategy to address
the unmet therapeutic needs of individuals afflicted by liver diseases. This approach identifies
shared genetic variants associated with common pathogenic mechanisms in both conditions. It also
expedites the development of new therapeutic options by identifying existing drugs that may be
effective in treating these liver diseases. Repurposing drugs is often more cost-effective and less
time-consuming than developing new drugs from scratch, making it an attractive strategy for
diseases with limited treatment options.
Objectives: To identify the genes which are present in both NAFLD and AFLD disease conditions.
Functional enrichment of those genes which could be used as potential targets. Identification of
the current drugs which could be repurposed for NAFLD and AFLD
Materials and methods: Disease associated genetic variants and their mapped genes were
collected from surveying the data available in the GWAS catalog portal by using the keywords
“NAFLD” and “AFLD”. PheWAS catalog was used to discover all associated genes corresponding
to variant alleles. The data was collected by setting the phenotype as "alcoholic liver damage" and
"non-alcoholic liver disease". The genetic variants and the mapped genes for these phenotypes
along with the associated diseases were retrieved. Transcriptome data of both NAFLD and AFLD
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disease conditions were retrieved from GEO database. These data were analysed using DESeq2
package in R to identify the differential expressed genes (DEGs). The DEGs were then sorted into
up and down regulated based on their logF2 value. All differentially expressed genes of both
disease conditions collected from GWAS, PheWAS and Transcriptome data were used in venn
diagram to identify the genes which are common for both conditions, for drug repurposing.
Pathway enrichment analysis was done for the genes which were discovered to be common
between both conditions using ‘Enrichr tool’. DrugBank 5.0 and DGIdb were used to identify the
FDA approved drugs in the market which targets the genes that were identified and that could be
repurposed.
Results and Discussion: Three hundred and twenty-eight common gene targets between NAFLD
and AFLD were identified using GWAS and PheWAS and transcriptome data through venn
diagram. Pathways implicated in the pathophysiology of AFLD and NAFLD were also identified
by integration of all the above data. From the KEGG pathway analysis, it was found that these
genes were involved in pathways which were found in other metabolic disorders such as diabetes,
obesity and cardiovascular diseases. Findings were used to identify potential targets for drug
repurposing. Since these disorders overlaps with other metabolic disorders, FDA approved drugs
from DrugBank 5.0 common for the above conditions were chosen for drugs repurposing. For each
potential gene target such as PNPLA3, drug repurposing was done through Cmap tool and the
those which were having a score of above 99 were chosen as drug candidates which could be
repurposed for fatty liver disorders.
Conclusion: Non-Alcoholic and Alcoholic Fatty liver disease represent a significant global health
burden, with prevalence of 32.4% and 5.1% respectively. Insulin sensitisers and lipid lowering
drugs are currently used along with diet and lifestyle modification. Despite the rising incidence,
there is no FDA approved medications for these conditions. Results from this study, could provide
a valuable resource for drug repurposing efforts in the field of liver diseases, potentially
accelerating the development of new treatment options for NAFLD and AFLD. This work
underscores the importance of utilizing large-scale genomics data to uncover novel therapeutic
approaches and improve treatment options in the context of these prevalent and challenging liver
diseases. These should be validated clinically for their effectiveness.
Keywords: NAFLD, AFLD, GWAS, PheWAS, Drug Repurposing
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Drug Repurposing Analysis for Psoriatic arthritis using PheWAS and
Transcriptome Data
Jayasree N, Dharuunya H V and Selvi Subramanian
Department of Biotechnology, PSG College of Technology, Coimbatore
selvi.bio@psgtech.ac.in
Abstract
Rational: The conventional method of drug development requires more time and cost. Drug
repurposing reduces drug development timeline as various existing compounds have already
demonstrated safety in humans and it does not require Phase 1 clinical trials. With the advancement
of bioinformatics/cheminformatics tools and availability of huge biological and structural
database, drug repositioning has significantly decreased the time and cost of the drug development
with reduction in risk of failure. It confers reduced risk of failure where a failure rate of 45% is
associated due to safety or toxicity issues in traditional drug discovery program with additional
benefit of saving up to 5–7 years in average drug development time. Since psoriasis and psoriatic
arthritis are associated the drugs for psoriasis could also be repurposed and used for psoriatic
arthritis
Objective: To identify potential target genes for psoriatic arthritis and psoriasis. To identify drugs
from drug bank against each target gene. Drug repurposing analysis for target genes
Methodology: In this study we used wide range of data retrieved from GWAS, and PharmgKB
datasets for identification of reported gene targets for Psoriatic arthritis and Psoriasis. In addition,
comparative gene expression analysis was performed on the transcriptomic dataset obtained from
PsA and psoriatic conditions. The potential gene targets from the above study were subjected to
logical distribution analysis using Omics box tool. Functional enrichment analysis was performed
for the target genes using enrichr tool to understand the disease pathogenesis. The drug targets for
these candidate genes were analyzed using drug bank database for drug repurposing
Results and Discussion: Target genes were identified using the available pharmgkb, GWAS and
transcriptomic data for PsA and psoriasis. Forty-nine common gene targets associated with the
disease were identified using Venn diagram. The pathway enrichment analysis led to a better
understanding about the disease pathogenesis. Involvement of DEGs in the KEGG pathway was
explored further. The targets genes of Psoriatic arthritis and Psoriasis involved in various
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pathways. Hence, we have a wide option in treatment of the disease. The drugs for the target genes
were identified through drug bank and drug repurposing was performed. For the target gene
TNFAIP3, drug repurposing was performed using Cmap tool and the selective match with the
score of above 99 were chosen to be the potential drug candidates. These could be further analyzed
and implemented for drug usage. Likewise, drug repurposing was done for each reported gene
targets which would enable wide range of treatment options.
Conclusion: Psoriasis is a multisystemic, chronic inflammatory skin illness characterized by scaly
erythematous plaques on the extensor surfaces of the elbows and knees and 20-30% of psoriasis
patients develop psoriatic arthritis (PsA). As the duration for drug development for a disease is
high, treating the disease can be costly and painful. Drug repurposing is an alternative to reduce
the duration and cost of drug development. Using the available pharmgkb, GWAS and
transcriptomic data for PsA and psoriasis, 49 common gene targets associated with the disease
were identified. The pathway enrichment analysis led guided to acquire knowledge about the
disease pathogenesis. The drugs for the target genes were identified through drug bank and drug
repurposing was performed for the target TNFAIP3 which could be an alternative drug candidate
for PsA enabling wide range of treatment options.
Keywords: Psoriatic arthritis, Psoriasis, Repurposing, Gene expression analysis, GWAS,
Transcriptomics
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Multi-Omics Analysis to Unravel the Molecular Etiology of Migraine-
Related Psychiatric Disorders
Hema Palanisamy, Jayavishnu Elanchezhian and Selvi Subramanian
Department of biotechnology, PSG college of Technology, Coimbatore
selvi.bio@psgtech.ac.in
Abstract
Introduction: Migraine (MIG) is neurological disorder that is prevalent among ~15% of general
population. It is characterized by recurrent attacks of headaches along with symptoms like nausea,
vomiting, hypersensitivity to light and sound. It can occur at any stage of life span including
children, adult and elder individuals. Both genetic and environmental factors contribute to the
development of MIG attack. Multigenetic variants influence the development MIG when
compared with the variants in individual genes which is identified through Genome-wide
association studies (GWAS). It is hypothesized that there is a shared genetic association between
MIG and other psychiatric disorders. But the etiology of the MIG and associated disorders remain
elusive. Advancements in omics technologies and availability of omics data could help to unravel
their etiology and association for better therapy. In this study we implemented multi-omics
approach to study the etiology of MIG associated two most common mental disorders
schizophrenia (SCN) and depression (DEP).
Methodology: Variants and genes associated with MIG and SCN, MIG and DEP were retrieved
from literature. The gene expression data for SCN and DEP was collected from published source
through Gene Expression Omnibus (GEO) datasets. The studies which utilized expression
quantification through microarray across various tissue samples were selected. The datasets were
analyzed to identify Differentially Expressed Genes (DEGs) through ‘GEO2R’ for SCN and DEP
separately. DEGs were filtered which have p-value <0.05 were selected further. Genes which
had positive logFC value were up regulated genes and genes which had negative logFC were down
regulated genes. Expression of genes identified through variant analysis for SCN and DEP were
filtered and taken for enrichment analysis using ‘Enrichr’ web tool.
Results and Discussion: From literature we identified 37 genes from 14 loci associated with MIG
and DEP and 298 genes for 36 loci associated with MIG and DEP. Since these genes are jointly
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associated with MIG, they can be involved in MIG associated SCN and DEP. Hence, these genes
were considered as candidate genes.
Expression of candidate genes involved in MIG associated SCN and DEP were identified
through expression profiling datasets of SCN and DEP. For SCN, we identified ten DEGs in
prefrontal cortices. ARMC6 was highly upregulated and rs11668203 variant was associated with
the gene. Twenty-one DEGs in olfactory neuronal cells and UBALD1 gene associated with
rs4786505 variant was downregulated. Eighteen DEGs in superior temporal cortex among which
CNNM2 was moderately upregulated and NACA was downregulated. Thirteen DEGs in BA10
region from which NUP160 was upregulated and EFNA was downregulated. ARMC6 associated
with rs11668203 was upregulated across all tissues during SCN. It is reported that ARMC6 is
involved in regulating wnt signaling pathway that could lead to nervous system disorders. So, the
rs11668203 variant could be involved in SCN during migraine. GNE, DESI1 were upregulated in
DEP. rs71327107 variant was involved in both DEP and SCN. Involvement of DEGs in KEGG
pathway was explored further. Pyruvate metabolism was altered by MIG associated SCN genes.
Among peripheral metabolites, pyruvate is a significant substrate for brain and peripheral energy
metabolism. Thus, it could be associated with the pathophysiology of SCN. Amino acid and
nucleotide metabolism was altered during DEP. It is reported that the dysregulation of amino acids
has been consistently correlated with psychopathology.
Conclusion: We identified candidate genes and variants that could be involved in migraine
associated SCN and DEP. We further identified DEGs from the candidate genes and identified
their functional involvement. Hence these targets could be evaluated for their therapeutic efficacy
to treat migraine associated SCN and DEP. Further proteome and metabolome level analyses are
yet to be performed to validate their involvement at a higher resolution.
Keywords: Migraine, Depression, Schizophrenia, multi-omics, transcriptome
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Molecular Docking and Molecular Dynamic Simulation (MDS)
Investigation of Actinobacterial based Bioactive Compounds against
Fusobacterium nucleatum Aggravated Oral Squamous Cell Carcinoma
(OSCC)
Zarin Taj and Indranil Chattopadhyay,
Department of Biotechnology, School of Life Sciences, Central University of Tamil Nadu,
Thiruvarur, Tamil Nadu
indranil@cutn.ac.in
Abstract
Oral squamous cell carcinoma (OSCC) is the prevailing cancer impacting the oral cavity,
characterized by a dismal prognosis and a relatively low probability of survival. There exists a
strong association between the microbes with the OSCC. Among the periodontopathogenic
bacteria, Fusobacterium nucleatum (F. nucleatum) is considered a potential risk factor in the
progression of OSCC. Many natural bioactive compounds explored for their antimicrobial and
anticancer potentiality in a variety of microbiome-aggravated cancers but have rarely been studied
in oral squamous cell carcinoma (OSCC). The key virulence proteins of F. nucleatum including
FadA and Fap2 has been identified to bind host cell and activates various oncogenic pathways in
oral carcinogenesis. The current study employs High-Throughput Virtual Screening, ADME/T
profiling, Molecular docking, and Molecular Dynamic Simulation techniques to identify potential
actinobacterial bioactive secondary metabolites that can target therapeutic targets of both
pathogenic virulence proteins and host cell proteins. In this study, among 179 bioactive secondary
metabolites, AM-158 labelled metabolite exhibited multi-protein targeted with highly acceptable
binding affinity with drug-likeness property and passed level of toxicity. Comprehensive docking
interaction of the best top-ranked metabolite AM-158 with OSCC-related protein targets illustrated
greater binding affinity towards E-cadherin and p38 proteins. The molecular dynamic (MD)
simulation has been executed for the metabolite AM-158 for both bacterial virulence proteins and
cancer therapeutic targets showing stable intermolecular binding with both hydrogen and
hydrophobic interactions. In conclusion, this study states that bioactive secondary metabolite AM-
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158 from Actinobacteria could be a powerful therapeutic compound to treat Fusobacterium
nucleatum aggravated oral squamous cell carcinoma (OSCC).
Keywords: Actinomycetes, Bioactive metabolites, antimicrobial, anticancer, oral squamous cell
carcinoma, Fusobacterium nucleatum, molecular docking, molecular dynamics
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Computational Screening and Docking analysis of Phytochemicals from
Senna auriculata and Trachyspermum copticum against Multiple
targets of Mycobacterium tuberculosis
Neeraja Karumarathodi, Shreya Manjusha Binukumar, Subhankar Das, Sadhana Sundararajan,
Keerthana Karunakaran and Rajiniraja Muniyan.
School of Biosciences and Technology, Vellore Institute of Technology, Vellore
rajiniraja.m@vit.ac.in
Abstract
Tuberculosis is an infectious disease caused by the potential pathogenic bacteria Mycobacterium
tuberculosis which causes more than ~1.5 million deaths every year. The reason for its successful
infection rate is owed to its resilient, tough mycolic acid-rich cell wall that makes the antibiotics
hard to penetrate into the cell and its ability to manipulate the immune system. In addition, drug
resistance has become a major concern. For the above-mentioned reasons, incessant attempts are
being made to identify novel drug targets and newer natural anti-tubercular drugs to control the
spread of TB. In the previous study, ethnobotanically important medicinal plants such as
Trachyspermum copticum and Senna auriculata were evaluated for anti-mycobacterial potential
against M. smegmatis. The ethyl acetate and methanol extracts of the selected plants that had anti-
TB activity were analyzed in Gas Chromatography-Mass Spectrometry (GC-MS) to identify the
compounds responsible for the activity. In the current study, a total of 53 phytochemicals identified
and mentioned in literature from medicinal plants Trachyspermum copticum and Senna auriculata
in addition to the phytochemicals obtained from the GC-MS analysis were subjected to multi-step
filtration protocol against eight drug targets of Mycobacterium tuberculosis. Important proteins
that serve as targets for front-line drugs owing to their importance in arresting the growth of the
organism are considered in the study as targets for natural compounds from S. auriculata and T.
copticum. Various proteins play significant roles in different pathways contributing to cell wall
metabolism which makes them possible drug targets. A multi-targeted approach of natural plant
compounds against the front-line drugs is attempted in this study. The compounds selected for the
study were filtered using Lipinski’s rule of five and the docking procedure was validated using co-
crystallized ligands and the drugs, in case of the absence of co-crystallized structures. The proteins
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for the study were taken from PDB based on resolution, absence of mutation, and residues present.
The process of docking was done using Autodock 4.2. From the exhaustive docking analysis,
stigmasterol was identified as potential smulti-targeting compound that was found effective against
multiple emerging targets such as EmbC, FbiB, and MmpL3 of Mycobacterium tuberculosis.
Subsequently, the molecular dynamic simulation was done to study the stability of the complex in
comparison with apoprotein.
Keywords: Multiple target approach, Molecular docking, Senna auriculata, Trachyspermum
copticum, Exhaustive docking.
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Exploration of Phytochemicals as Prospective Drug Candidates for
Tuberculosis: A Computational Approach
Faaizah Afsheen1, Srinivasan M.R2, Sujatha P.L3, Vijayarani K4, Devendran P5
Bioinformatics Centre, Madras Veterinary college, TANUVAS1,
Laboratory Animal Medicine Unit, CAHS, TANUVAS2,
Madras Veterinary College, TANUVAS3,
TANUVAS, Madhavaram Milk colony4,
Bioinformatics Centre, Madras Veterinary college, TANUVAS5
seenubioinfo@gmail.com
Abstract
Motivation: Tuberculosis remains a significant global public health challenge with drug resistance
threatening the use of the first-line anti-TB drugs, ethambutol, isoniazid, rifampicin and
pyrazinamide. This requires the development of novel drugs with fewer side effects.
Objectives: This study aims to assess the potential of phytochemicals as drug candidates for the
treatment of tuberculosis through the integration of systems biology, molecular docking, and
molecular simulation methodologies.
Materials and Methods: Phytochemicals of nine plants exhibiting in vitro anti-tubercular
activity—Curcuma longa, Ocimum basilicum, Opuntia ficus indica, Mangifera indica, Vitex
negundo, Tinospora cordifolia, Justicia adhatoda, Acacia catechu, and Mukia maderaspatan were
obtained from documented references, and their structures were retrieved from PubChem. Around
75 ligands with logP values between 0.5 to 5, zero PAINS alerts, predicted non-mutagenicity,
solubility, and intestinal absorption were selected following ADMET prediction in Discovery
Studio 2020 and SwissADME. Mycobacterial proteins spanning the pathogenic genome were
acquired from the Mycobrowser data repository and documented sources. A protein-protein
interaction network was constructed in STRING with the 900 proteins. An interaction network
with 634 nodes was created and analyzed in Cytoscape 3.0 and NetworkX. The proteins with the
highest betweeness scores in the entire network and the proteins with the highest betweeness scores
in each of the isolated networks were identified. The 3D structures of these proteins were obtained
from PDB and AlphaFold databases. Binding energies of the identified compounds and
mycobacterial protein complexes were calculated in Discovery Studio 2020 after docking with
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CDOCKER protocol and were compared with that of the first-line drugs. The functions of the
proteins were confirmed with enrichment analysis in DAVID. The compound- protein complexes
for molecular dynamics were selected based on their relevance to mycobacterial pathways and
their binding energies and were simulated in Discovery Studio 2020 for 20 ns to identify the most
favorable compounds and interactions for subsequent drug development endeavours.
Results: The compounds showed higher affinity to the drug targets rpoB, inhA and embC which
were among the identified 45 central proteins in the network. The compounds, curcumin, 1,2-
dihydrocurcumin, tetrahydrobisdemethoxycurcumin, ferulic acid, isorhamnetin, xanthomicrol,
tolycaine, bisdemethoxycurcumin, 3-(2,4-dihydroxyphenyl)-acrylic acid, iriflophenone, N-trans-
Feruloyltyramine, 4-hydroxybenzoic acid and 4-hydroxybenzaldehyde exhibited the lowest
binding energies across all the proteins. The conformational change of the highest scoring complex
was observed to be >6 A.
Keywords: Network analysis, Phytochemicals, Tuberculosis
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To Identify a Novel Target Compound to Inhibit the OXA β-Lactamases
Causing Extremely Drug-Resistant Hospital-Acquired Infections
Mohanraj Gopikrishnan1, Sriroopreddy Ramireddy1, Yamuna Devi Bakathavatchalam2,
Thirumal Kumar D3, Binesh Lal Yesudoss4, Abi Manesh5, Kamini Walia6, Balaji
Veeraraghavan4, George Priya Doss C1
Department of Integrative Biology, School of Biosciences and Technology, VIT, Vellore, Tamil
Nadu, India1,
Department of Clinical Microbiology, Christian Medical College, Vellore, Tamil Nadu, India2,
Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, India3,
Department of Clinical Microbiology, Christian Medical College, Vellore, Tamil Nadu, India4,
Department of Infectious Disease, Christian Medical College, Vellore, Tamil Nadu, India5,
Division of Epidemiology & Communicable Diseases, Indian Council of Medical Research, New
Delhi, India6
georgepriyadoss@vit.ac.in
Abstract
Carbapenem-resistant Acinetobacter baumannii (CRAB) and Carbapenem-resistant
Enterobacteriaceae (CRE) is an important nosocomial infection in healthcare sectors, and it leads
to higher rates of morbidity and mortality rates. It causes mostly bacteremia and ventilator-
associated pneumoniae. The presence of Oxacillinases leads to a high drug-resistant profile, and it
is mostly resistant to β-lactams. Currently, there is no proper drug regimen against the carbapenem
resistance. This necessitates the need to search for newer approaches. Recently, small molecules
have shown more potent activity against multi-drug resistance strains. To this aspect, we collected
nearly 28,831 compounds in the Antibacterial Library from Enamine to design for the development
of novel antibacterials against the Oxacillinases. So, we computationally performed the high
throughput virtual screening of these compounds through Schrodinger platforms against the
targeted Oxacillinases such as (OXA‐23/ 24/58 like) expressing A. baumannii and OXA-181/232
from E. coli and K. pneumonia isolates. To further validate our findings, molecular dynamic
simulations were performed using GROMACS followed by molecular mechanics Poisson–
Boltzmann surface area (MM-PBSA) analysis. To further prove our hypothesis, the potent
compounds were synthesized, and we determined the minimum inhibitory concentration (MIC)
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using Microbroth dilution methods. In the virtual screening, we selected the top two compounds
of Z1738535794, and Z863012955 which showed the higher binding energy of -6.0 to -7.0
kcal/mol against all the variants of Oxacillinases and these compounds passed the ADMET
properties. Then we performed the molecular dynamic environment for 100 ns all the complexes
were stabled in the dynamic environment. Then we did an MMPBSA analysis it has a total binding
affinity ranging from -81.03 to -94.06 KJ/mol. Whereas in MIC testing, the obtained two
compounds were effective against Oxacillinases. The obtained potent compound is a promising
drug candidate for treating Oxacillinases expressing resistant bacterial strains.
Keywords: Oxacillinases, Betalactam, Antibacterial library, Schrodinger, Molecular dynamics
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Differential Gene Expression Analysis to Unveil Potential Targets and
Pathways in Atherosclerosis Under Prolonged Hyperglycemic
Conditions: A Bioinformatics Study
Soumya Jal1, Sangeeta Chhotaray2, Kanika Verma3, Nitesh Dhar Badgayan1
Dean, School of Paramedics and Allied Health Sciences, Centurion University of Technology
and Management, Odisha1
Research Scholar, SoPAHS, Centurion University of Technology and Management, Odisha2,
Scientist B, Department of Molecular Epidemiology, ICMR-National Institute of Malaria
Research3
soumya.jal@cutm.ac.in
Abstract
Background and Aim: Persistent elevation of blood glucose levels can result in impairments to
vascular and neural systems, contributing to the development of cardiovascular and multi-organ
complications. Atherosclerosis, being one such complication, is characterized by the accumulation
of fatty deposits within the arteries. The weakening of the myocardium, which is caused by
prolonged hyperglycemia, is a major contributor to global mortality. However, the precise
mechanisms by which diabetes leads to cardiac complications are poorly defined. This study aims
to employ bioinformatics methods to screen and identify molecular targets that correlate
proliferative Diabetic Retinopathy (DR) with atherosclerosis.
Methods: The analysis utilized transcriptomic data obtained from the GEO database, specifically
the dataset with accession number GSE94019. The GEO2R statistical tool was employed to
identify differentially expressed genes (DEGs) in the Endothelial Cells from Fibrovascular
Membranes of nine individuals with diabetes and four individuals without diabetes. The
identification of the interaction between the differentially expressed genes (DEGs) was performed
by utilizing the STRING tool, followed by visualization using the Cytoscape software. In order to
identify the gene cluster within the interactive networks, Cytoscape was utilized. Functional
annotation of the identified DEGs was performed using the DAVID web server and Shinygo tool.
This included gene ontology (GO) and enriched molecular pathway analysis of DEGs.
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Result: We examined the 414 most significant DEGs (p-value < 0.05) out of a total of 3765 DEGs.
DEGs that exhibited significant differences in GO analysis were identified as being implicated in
molecular pathways and critical biological processes, including calcium ion binding, cell adhesion,
and vascular smooth muscle cell proliferation. The correlation between DEGs and the MAPK
signaling pathway was identified through the examination of enriched KEGG pathways. It has
been demonstrated that the genes implicated in the molecular pathways can be selectively
regulated through the activation or inhibition of genes that are indispensable for the canonical
signaling pathways. Ten hub genes (TP53, JUN, CD4, PTEN, ICAM1, GRB2, CREBBP, TFRC,
EWSR1, and CDKN1A) were identified in our research as being significantly associated with DR
and a heightened susceptibility to atherosclerosis.
Keywords: Atherosclerosis, Diabetic Retinopathy, STRING
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Antiproliferative Activity of Prodigiosin Derived from Serratia
Marcescens VITSD2: An In Vitro & In Silico Approach
Sreelakshmi R Nair and Subathradevi C,
Vellore Institute of Technology, Vellore
csubathradevi@vit.ac.in
Abstract
Prodigiosin is a potent anti-oxidant, red pigment produced by different strains of Serratia
marcescens and other bacteria. The bio pigment demonstrates many hopeful impending
bioactivities. It was found to be an active proapoptotic agent against multiple cancer cell lines. In
the present study, pigment produced from soil isolate Serratia marcescens VITSD2 was
characterized and identified using UV, FTIR, GC-MS and NMR analysis (1H NMR and 13C
NMR). Prodigiosin pigment produced from Serratia marcescens VITSD2 showed potent
cytotoxicity on HepG2 cancer cells. The anti-proliferative activity of prodigiosin pigment from
Serratia marcescens VITSD2 was evaluated on cancer cell lines. The active sites and binding
patterns of molecular marker survivin was analysed on docking against prodigiosin. A strong
antioxidant potential was noticed at 5mg/mL concentration with 70±0.08% scavenging activity
(2,2-diphenyl-1-picrylhydrazyl)-DPPH. The dose dependent inhibition of HepG2 cell proliferation
was observed maximum with 67±0.08% cytotoxic activity at 50 µg / mL. When compared to other
cell lines, A549, HL 60 and MCF-7, prodigiosin had a strong inhibitory activity on HepG2 cells.
Serratia marcescens VITSD2 showed potent cytotoxicity on HepG2 cancer cells A single band
with an Rf value of 0.45 was observed after chromatography. Maximum absorbance was observed
at 535 nm. The pigment revealed the characteristic functional properties of the prodigiosin. On
docking, the lowest binding energy exhibited was found to be -6.78 kcal/mol. The RMSD analysis
indicated that the backbone structure converges at 18ns before it attains stability. The study
outcomes specified that the bio pigment prodigiosin extracted from Serratia marcescens VIT SD2
is a promising drug candidate appropriate for therapeutic applications.
Keywords: Prodigiosin, Serratia marcescens VITSD2, antioxidant, pigments, Anti-cancer, Hep-
G2
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Virtual Screening, Molecular Docking and Dynamics Simulations for
Identifying Potential Natural Inhibitors for Managing Colorectal
Cancer against PDZ Domain-Containing Protein GIPC2
Karishma Sahoo and Vino Sundararajan,
Vellore Institute of Technology
svino@vit.ac.in
Abstract
Motivation: Colorectal cancer (CRC), affects the population with prevalent malignancies, high
mortality rate, and poor prognosis of patients accounting for the targeted therapies that can
successfully increase the overall survival rate of patients. Furthermore, conventional cancer
therapies have presented numerous challenges, such as toxicity, resistance to multiple drugs, and
high financial burden. Conversely, there's a growing interest in the world of complementary
alternative medicine, specifically in bioactive phytochemicals. These compounds have captivated
our attention by their ability to influence a wide range of molecular processes while minimizing
harmful side effects. Moreover, natural products have played a critical role in medicine showing
the anti-apoptosis, anti-oxidative, pro-apoptotic, and anti-metastatic activity and their ability to
bind and modulate cellular targets involved in disease. Therefore, this study was planned to screen
phytochemicals against PDZ domain-containing protein GIPC2 which has the potential to
modulate the signaling pathways involved in cancer and may be used for developing an effective
and broad-spectrum strategy for increasing the overall survival, progress-free interval, TNM
staging of CRC patients, in coming future.
Objectives: The identification of potent inhibitors from the large diverse natural compound library
using the target based virtual screening process. Quantify the binding affinity along with the
prediction of the potential bioactivity and pharmacological properties of the screened compounds,
including ADME (Absorption, Distribution, Metabolism, and Excretion) properties. Perform
molecular dynamics simulations to assess the binding stability, dynamic behavior and interactions
between the shortlisted plant compounds and the targeted protein.
Methodology:
3.1. Datasets construction of natural compounds:
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Almost 5006 natural anticancerous compounds were collected through a literature survey and
databases such as PubChem, and IMMPAT (Indian Medicinal Plants, Phytochemistry And
Therapeutics) and where identified showing the inhibitory properties against PDZ domain-
containing protein GIPC2. Few of the FDA approved inhibitors were retrieved by the same
procedure and used for docking against the protein.
3.2. Target-based Virtual screening and Determination of ADMET, Lipinski’s rule, and
pharmacokinetics of screened compounds:
The ligands and the proteins were prepared for Target-based Virtual screening and the top 15
screened compounds were subjected to ADMET, Lipinski’s rule, and pharmacokinetics analysis.
3.3. Molecular docking and Molecular Dynamic simulation:
Molecular Docking for single ligand and protein complex can validate the results of Auto dock
vina results which can be further subjected to Molecular dynamics simulations using the
GROMACS package to determine structural stability and protein properties followed by the free
binding energy calculation. This simulation can be carried out for 500ns using CHARMM36 force
field in GROMACS. Furthermore, the post simulation analysis such as Solvent Accessible Surface
Area (SASA), Radius of Gyration, Root Mean Square Deviation (RMSD), Root Mean Square
Fluctuation (RMSF), and visualization of all graphs can be generated using Xmgrace.
Results: To decipher the anticancerous activity of the selected natural products against the receptor
protein (PDB ID- 3GGE), high throughput virtual screening was performed via Autodock Vina
with a library comprising 5008 natural compounds showing anticancerous activity. The top 10
screened compounds were seen to have good docking scores and were considered to be showing
better inhibition protein GIPC2 which are Oleanolic Acid, alpha-Amyrenone, Ursolic acid, alpha-
Amyrin, Cimigenol, Lupeol, Bismorphine B, Gochnatiolide A, Irinotecan, and 9-Nitroamino-
camptothecin. Also, the pharmacokinetic data of the screened compounds should demonstrate that
all chosen natural products exhibit improved pharmacological characteristics, such as low
molecular weight, compliance with the Lipinski rule of five (not exceedingly more than one
violation or showing no violation), favorable absorption profiles, oral bioavailability, excellent
gastrointestinal absorption, and minimal toxicity risk. Furthermore, these compounds predicted to
possess reasonably good pharmacological profiles should also surpass the insignificant toxicity.
Thereafter, alpha-amyrenone and oleanolic acid shows the highest binding energy, good human
intestinal absorption, good water solubility, and plasma protein binding affinity which is further
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selected as the best docked compounds. Finally, Molecular dynamics simulations for 500 ns
including molecular mechanics Poisson-Boltzmann (MMPBSA) calculations are expected to
provide strong evidence of the stability and integrity of the ligand-target protein complexes, further
affirming the validity of their binding mechanism.
Conclusion: Our research highlights the role of alpha-Amyrenone and Oleanolic acid as a
promising candidate for inhibiting the overexpression of GIPC2 protein, which also demonstrates
their action on inappropriate activation of WNT-signaling cancer pathways.
Keywords: Virtual Screening; Molecular docking, MM-GBSA, ADMET, Molecular dynamics,
Natural inhibitors, Colorectal cancer.
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An Integrated Bioinformatic Approach to Identify Potential Genes in
Colorectal Cancer
Soundharya G, Vyshali G and Shobana S,
Department of Biotechnology, PSG College of Technology
sbs.bio@psgtech.ac.in
Abstract
Colorectal cancer is one of the most frequent cancers in the world. Only a small percentage of
patients are being cured by current treatments, which work best for those with early-stage disease.
Therefore, treatment like gene therapy could be a possible way in the future to cure several types
of cancer. Identifying the origins and pathology as well as developing novel biomarkers are of
considerable significance and are urgently required due to the high incidence and mortality of
colorectal cancer. Henceforth, to identify key genes of colorectal cancer, we have performed a
gene expression analysis using a dataset (GSE223118) from Gene Expression Omnibus (GEO)
database. Differentially expressed genes (DEGs) were identified through the GEO2R analysis tool.
The network of protein–protein-interaction (PPI) was established by using the STRING database
and visualized by Cytoscape. A total of 1000 significantly differentially expressed genes were
obtained, which consisted of 362 up-regulated genes and 638 down-regulated genes. The
functional categories of the genes were identified using DAVID server. Most of the corresponding
genes were involved in the process such as signaling receptor activity, G protein-coupled receptor
binding activity and cytokine mediated signaling pathway. This study attempted to find further
molecular changes in colorectal cancer, requiring the development of novel diagnostic biomarkers
and therapeutic targets.
Keywords: Colorectal cancer, GEO, Biomarkers, protein-protein interaction
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Flux Balance Analysis of Exopolysaccharide Biosynthesis in
Methylobacterium mesophilicum
Vineeth L, Soundharya G and Shobana S
Department of Biotechnology, PSG College of Technology
sbs.bio@psgtech.ac.in
Abstract
Methanol, a petrochemical industry building block and a widely accessible and potent single
carbon liquid molecule that can be produced from methane present in natural gas, has emerged as
a potential candidate with low cost and high purity. Methylobacterium mesophilicum are pink
pigmented facultative methylotrophic (PPFM) methanol utilizers that can grow on single carbon
compounds such as methanol and formamide. They produce the exopolysaccharide (EPS), which
forms highly viscous aqueous solutions even at low concentrations, which is appealing to the food,
cosmetic, and pharmaceutical industries. Bacterial EPSs' high production capacity, low resource-
intensiveness, physicochemical and structural properties make them appealing for a wide range of
industrial applications. In this study we have developed a model for the biosynthesis pathway of
EPS in M. mesophilicum and performed flux balance analyses (FBA) for maximizing the
production of EPS using COBRA tool box. The model for EPS biosynthesis through colonic acid
pathway involves 16 metabolites and 12 reactions catalysed by 11 proteins. This study
demonstrates the usage of FBA for modelling the metabolic pathway thereby increasing the
production of exopolysaccharides.
Keywords: Methanol, FBA, COBRA, Exopolysaccharides, Methylobacterium
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Evaluation and Benchmarking of de novo Assembly Tools for
Prokaryotic Long Reads from Oxford Nanopore Technologies
Megha S Kumar, Suneesh P V, Aarathi Pradeep and Satheesh T G
Amrita Vishwa Vidyapeetham
sk_megha@cb.amrita.edu
Abstract
Rationale/Motivation: Reliable genome reconstruction from the sequenced data is vital for
several downstream genome analyses as it forms the base for studies such as gene annotation,
comparative genomics, and identification of variations and regulatory elements. Long-read
sequencing technology has enabled real-time generation of reads spanning thousands of
nucleotides with the maximum reported length exceeding 2 Mb. However, the error rate of the
long reads is generally higher when compared to the Illumina short reads. Over the years, several
long-read assemblers have been developed, based on different assembly algorithms, to overcome
this deficit and generate improved and complete assembly. With this increase in the choice of
computational tools, researchers face the dilemma of opting for the most suitable tool for their
study. Therefore, it is imperative to compare the performance of the tools using a common dataset
and certain evaluation metrics to identify a reliable approach for a specific study.
Objectives: The main aim of this study was to assess the assembly tools in the context of
prokaryotic genome assembly utilising long-read data generated by Oxford Nanopore Technology
(ONT) Sequences. The evaluation is based on accuracy, structural contiguity and performance
metrics. Additionally, the study aims to devise an optimised and reproducible assembly pipeline
informed by the outcomes of the evaluation, with the goal of achieving accurate, comprehensive
genome assemblies for prokaryotic organisms.
Materials and Methods:
Data description: The evaluation study focused on assembling two different strains of Escherichia
coli using reads generated by ONT. The first strain, E. coli ST131, is a pathogenic strain known
for causing extraintestinal infections associated with urinary and bloodstream infections. The reads
for this strain were obtained from the European Nucleotide Archive (ENA) and had the accession
number ERR3284704. For the second strain, E. coli DH5alpha, a non-pathogenic strain, the
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genomic DNA was extracted in-house and sequenced using our facility's ONT’s portable
sequencer, MinION.
Methods: The data was assembled using ten long-read assemblers, namely Canu, Flye, Hinge,
Miniasm, NECAT, Nextdenovo, Raven, Smartdenovo, Shasta and wtdgb2, with their default
parameters to assemble the data. The assemblies were evaluated using many criteria like
contiguity, genome completeness and performance attributes like run-time and resource usage.
Furthermore, the impact of preprocessing and polishing of the data on assembly was examined.
Results and Discussion: While we evaluated the tools based on factors such as contiguity,
accuracy and computational requirements, all the assemblers chosen were open-access, easily
installable and relatively user-friendly. Computational resource requirements are essential for
performing assemblies. More extensive run time and high computational demand can limit the
tool's usability. Under limiting resource assignment, Canu had the longest run time, whereas
Minisam had the least. Shasta, Redbean, NextDenovo and Miniasm were in the fastest category,
while Hinge, NECAT, Raven and Flye were middling, and Canu and SMARTdenovo were in the
slowest.
Contiguity analysis of the genome was based on the metric N50, though it does not provide an
accurate assessment of the performance of the assembler. The N50 of the data was more or less
the same for different assemblers. The assembly with the largest N50 may still have
misconnections, resulting in low-quality assemblies. Therefore, another metric, gene completeness
using BUSCO, was used to evaluate the quality of the assemblies. NECAT, Canu, NextDenovo
and SMARTdenovo showed excellent results in this regard.
It is important to note that preprocessing and post-processing of the assemblies significantly impact
the quality. Preprocessing would include eliminating low-quality reads for assembly and error
correction based on consensus. Some tools like Canu, NECAT and NextDenovo included an error
correction module, that allowed preprocessing of data. On the other hand, certain tools like Flye
and Smartdenovo provide consensus polishing feature that involves correcting the assemblies with
the raw reads, a post-assembly process.
Keywords: ONT long-reads, genome assembly, prokaryotes, pipeline, benchmarking
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Targeting Active PAK1 and γH2AX to Evade Radiation Resistance in
Head and Neck Squamous Cell Carcinoma
Swetha Rajendran1, Vishnupriya K2, Micheal Gromiha2, Suresh Kumar Rayala2 and Ganesh
Venkatraman3
Department of Human Genetics, FBMS&T, Sri Ramachandra Institute of Higher Education and
Research1, Department of Biotechnology, IIT-Madras2, Department of Biomedical Sciences, SBST, VIT-
Vellore3
rayala@iitm.ac.in
Abstract
Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common cancer worldwide.
HNSCCs are usually diagnosed at later stages and are given with multimodal treatment (Surgical
resection and CRT). Still, HNSCCs are highly aggressive cancers showing higher rates of distant
metastasis and recurrence due to various reasons such as formation of multiple primary tumors at
multiple sites (second primary tumors) via field cancerization, resistance to chemotherapy and
radiation therapy. With literature evidence, we are aware that the oncogenic serine threonine kinase
called p21 activated kinase 1 (PAK1) is altered in multiple cancers and it plays a major role in
chemoresistance in breast, lung, and pancreatic cancer. Hence, we attempt to unravel the role of
PAK1 upon radiation exposure in HNSCC. All the data generated were statistically analysed using
student’s T test in GraphpadPrism software. We confirm that PAK1 is overexpressed in HNSCCs
cell lines compared to normal using western blot. It has been validated in patient's samples, where
PAK1 expression is significantly increased in HNSCC tissues (p value < 0.0001) compared to
normal. Upon exposure to ionizing radiation, PAK1 gets activated in HNSCC. The cells exposed
to different doses of ionizing radiation and tissue samples of patients underwent radiation therapy
were used to analyze the activated PAK1 levels using western blot, IHC and in-vitro kinase assay.
It is evident that the pPAK1 levels were significantly increased in patients with radiation therapy
(p value for pPAK1-S199 and pPAK1-T212 are 0.0012 and 0.0024 respectively) compared to the
naïve tissues. PAK1 being a central hub for the activation of many oncogenic signaling pathways,
and considering its role in cytoskeletal and ECM remodeling, EMT, cell cycle and apoptosis,
targeting active PAK1 helps in improving the radiosensitivity. This has been proved by various
functional assays in WT and PAK1 KO cells post irradiation. PAK1 knock out in SCC131 cells
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decreased the aggressive nature of cancer cells and increased the sensitivity to radiation therapy.
Since, we confirm PAK1 plays a major role in radiation response, we developed radioresistant
SCC131 cells (RR) to study the PAK1 status. pPAK1 levels are higher in RR cells and is associated
with aggressive phenotype of cells. We also confirm that the activated PAK1 is associated with
poor survival (p value = 0.016 for pPAK1-S199) in HNSCC patients using KM plot. Interestingly,
we found that the DNA damage marker – γH2AX is also elevated in radioresistant SCC131 cells.
This molecule acts as a docking nexus for many DNA damage repair proteins and promote non-
homologous end joining (NHEJ) or homologous recombination (HR). It is evident that elevated
PAK1 activity induces γH2AX via MORC2, thereby increasing the DNA repair process, ultimately
resulting in the survival of cells after exposure to ionizing radiation. Hence, we developed small
molecule peptide inhibitors against γH2AX that mimics its interacting partners to block the
recruitment of DNA repair proteins, thereby pushing the cells into radiation induced apoptosis. A
peptide called PL-8 that mimics MCPH1 was found to be highly potential in-silico. SCC131 cells
showed reduction in γH2AX upon treatment with PL-8 after exposure to ionizing radiation. As an
outcome of γH2AX inhibition, significantly increased DNA damage was observed using comet
assay in these cells indicating the effectiveness of γH2AX inhibition. Hence, along with PAK1
inhibition, γH2AX inhibition will help in sensitizing the cells to ionizing radiation, thereby
improving radiation response. Targeting active PAK1 and γH2AX ultimately decreases the rate of
recurrence and metastasis and improve the disease-free survival in HNSCC.
Keywords: Head and Neck Squamous Cell Carcinoma, Radiation resistance, PAK1, γH2AX,
Peptide inhibitors
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Unlocking New Avenues: Exploring Azoles, Statins, and Anti-cancerous
Compounds Against Mucormycosis for Drug Repurposing through
Molecular Docking
Nischay Biligi and Suresh P.K,
Department of Biomedical Sciences, School of Biosciences & Technology, VIT, Vellore, Vellore
p.k.suresh@vit.ac.in
Abstract
Background: Mucormycosis is a life-threatening fungal infection caused by ubiquitous,
filamentous fungi from the Mucorales order, primarily Rhizopus oryzae. It poses a significant
threat to life as it invades blood vessels. The major risk factors associated with Mucormycosis
include COVID-19 infection or cancer treatments resulting in neutropenia, organ or stem cell
transplantation, immunosuppressive therapies, corticosteroid treatment, diabetic ketoacidosis
[DKA], deferoxamine therapy, and high levels of free iron in the bloodstream. Despite the
availability of treatment options such as surgical debridement, antifungal therapy, and adjunctive
therapies, the mortality rates for Mucormycosis remain alarmingly high. This study delved into
various innovative therapeutic approaches to combat the challenges posed by Mucormycosis.
Results: The present study focused on the most crucial protein, Lanosterol 14 alpha-demethylase,
which plays a significant role in the survival of Rhizopus delemar. The investigation began by
analyzing the physiochemical, structural, and functional characteristics of this protein.
Subsequently, molecular docking utilizing AutoDock Vina was employed to explore the
interaction between the selected protein and a diverse dataset of compounds. The compounds
encompassed various categories such as FDA-approved drugs, FDA-unapproved drugs,
investigational-only drugs, and biologics, including azoles, anticancerous compounds, and statins.
Computational analyses were performed to estimate the ADMET parameters and biological
activity of these compounds.
Conclusions: Through our computational investigations, we identified nine primary ligands with
potential inhibitory properties against Rhizopus delemar. These ligands, namely Nilotinib,
Conivaptan, Atorvastatin, Lapatinib, Idarubicin, Irinotecan, Simvastatin, Saperconazole, and
Opelconazole, demonstrated inhibitory effects on Lanosterol 14 alpha-demethylase. These
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findings suggest that these compounds could be repurposed as potential drug candidates for the
treatment of mucormycosis. Further optimization, formulation techniques, and subsequent in vitro
and in vivo studies are warranted to explore the therapeutic potential of these compounds in greater
detail.
Keywords: Mucormycosis, Covid-19, Molecular docking, Virtual screening, Drug repurposing
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A Bio Inspired Sentiment Analysis of Social Media for Early Detection
of Negative Emotions in Young Adults
Neha Tyagi1, Deepshikha Bhargava1 Anil Ahlawat2
Amity University1, KIET Ghaziabad2
nhtyagi190@gmail.com
Abstract
This paper describes the reviews of deep learning techniques on Sentiment Analysis (SA).
Sentiment Analysis (SA) becomes more prominent development of Natural Language Processing.
It is mainly processed the sentiment of a text in an efficient manner and also it can perform social
media analytics. In recent times, the usage of DL approaches solves a variety of issues in sentiment
analytics. It can be directly applied on live data given that the feature set is large whereas, in the
DL method, the classifier requires to be initially nourished or “trained" with the raw datasets and
tune to cluster the sentiments into predefined classes. But it works efficiently on large texts with
large feature support. SA refers to the computational study that assesses people’s emotions and
opinions towards an entity. Sentiments are based on your emotions in any situation.
The objective of this paper to explore different methodologies used on sentiments and how can we
detect or comparison of emotions on online reviews by using different technologies. NLP (Natural
language process) technique is used to access classify or mining the text NLP has various types of
technologies by which text can be accessed and can be changed into the vector form by using
word2Vec.
Keywords: Deep learning, Neural network Natural Language Processing, social media
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Insights into the Role of Potassium Channels in Migraine
Girishwaran M and Sajitha Lulu S,
Integrative Multiomics Lab, School of Bio Sciences and Technology, Vellore Institute of
Technology
ssajithalulu@vit.ac.in
Abstract
Rationale/Motivation: Migraine is a primary headache disorder characterized by unilateral pain
usually with aura, that affects approximately 1 in 5 individuals in India (as per the Global Disease
Burden Survey, 2019). The underlying biomechanical processes of migraine are still poorly
understood, but novel discoveries and research are constantly being published. One of the major
factors in susceptibility to migraine is the dysfunction of ion channels in the trigeminal nuclei and
sensory cortices of the brain. While calcium channels and the sodium-potassium pump channels
are well described in relation to familial hemiplegic migraine, the current understanding of the role
of inheritable channelopathies and channel dysfunctions in familial and sporadic migraine with
aura is still obscure. Potassium channels are well known in neurology as modulators and regulators
of neuronal signaling and conductance, playing an important role in maintenance of the membrane
potential and in the passing of electric currents through nervous cells. Therefore, potassium
channel dysfunctions are potential causative or exacerbating factors in migraine pathogenesis, and
could provide a valuable novel target for specific antimigraine prophylaxis.
Objectives: The review focus to provide an extensive review of the current literature surrounding
the role of potassium channels in migraine
Methods: The authors collected articles from the PubMed literature database using the following
search terms: (potassium channels) AND (migraine), ((potassium channels) AND (migraine))
AND (treatment), (potassium channels) AND (channelopathies), (voltage gated potassium
channels) AND (channelopathies), (calcium gated potassium channels) AND (channelopathies),
(ATP sensitive potassium channels) AND (channelopathies), (two-pore domain gated potassium
channels) AND (channelopathies), and (inward rectifying potassium channels) AND
(channelopathies).
Results: A total of 1821 non-unique articles were found, which were reduced to 116 articles after
removing duplicate results, citations, irrelevant articles, and articles without a valid DOI index.
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The findings reported in these 116 articles were then summarized and discussed. Articles were
excluded for irrelevancy based on the following criteria:
1. Article Text mentioned neither migraine nor potassium channels in detail
2. Article text discussed migraine but did not make specific mention of potassium channels in
relation to migraine
3. Article text discussed potassium channelopathies but did not mention migraine in detail
Conclusion: This review reveals that potassium channels play a significant role in migraine
prognosis. Dysfunctions in KIR channels, K2P channels including TRESK and TREK-1, small
and large conductance calcium-sensitive potassium channels (SKCa and BKCa), and voltage-
gated potassium channels (KV) are known to affect the incidence and progression of migraine in
the general populace. KATP openers can induce migraine like phenotype, but KATP blockers have
so far not been effective in reducing the intensity of migraine headache. Potassium channels are a
potential druggable target for migraine prophylaxis with several compounds currently in
preclinical trials.
Keywords: Migraine, potassium channels, druggable targets, glibenclamide, levcromakalim,
acrylamide (S-1)
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Identification of CYP51 From Leishmania Major, Strain Friedlin as A
Potential Drug Target and Repurposing of FDA Approved Drugs
Against It to Uncover Drug Against It
Chandra Kanta Bhusal1, Upninder Kaur2 and Rakesh Sehgal1
Aarupadai Veedu Medical College and Hospital, Post Graduate Institute of Medical Education
and Research1, Post Graduate Institute of Medical Education and Research2
c.k.bhusal71@gmail.com
Abstract
Introduction: Cutaneous Leishmaniasis, mainly caused by Leishmania major, lacks reliable
treatment options. This study aimed to identify potential drug targets in Leishmania major (strain
Friedlin) and repurpose FDA approved drugs for treatment. Comparative metabolic pathway
analysis and selection criteria, including interactome, druggability, essentiality, and gene ontology,
identified lanosterol 14-alpha demethylase (CYP51) as a promising target.
Methodology: We conducted virtual screening of FDA approved drugs against CYP51 using
PyRx, assessed complex interactions in Biovia Discovery, and redocked for validation.
Isavuconazole and posaconazole emerged as top candidates for CYP51 binding. Fluconazole, a
known inhibitor of CYP51 was docked against the same as control. Molecular dynamics (MD)
simulations over 100 nanoseconds assessed complex stability. Root Mean Square Deviation
(RMSD), Root Mean Square Fluctuation (RMSF), and hydrogen bond analysis measured atomic
distances. Principal component analysis explored essential system dynamics on a low-dimensional
free energy landscape.
Results: Our study has identified CYP51 as an ideal drug target. Isavuconazole and posaconazole
emerged as the most promising candidates for CYP51 binding, exhibiting binding scores of -10.4
and -9.3, along with 3 and 5 hydrogen bonds, respectively. In contrast, the control, Fluconazole,
demonstrated a binding energy of -7.1 with only 1 hydrogen bond. Molecular dynamics (MD)
simulations subsequently confirmed the stability of the complexes, with root mean square
deviation (RMSD), root mean square fluctuation (RMSF), and hydrogen bond analysis supporting
the structural integrity. Principal component analysis provided valuable insights into the essential
dynamics of the system.
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Conclusion: This study suggests CYP51 as a promising target and identifies isavuconazole and
posaconazole as potential treatments for Cutaneous Leishmaniasis. MD simulations and analysis
indicate complex stability and dynamics, highlighting avenues for effective treatment
development.
Keywords: Virtual Screening, Docking, MD Simulation, Computer-aided Drug Design
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Unmasking Alzheimer's Central Players: A Network Perspective
Sanga Mitra1, Carlos Viesi1, Marcus Seldin2 and Manikandan Narayanan1
Indian Institute of Technology (IIT) Madras, Chennai, India1
Department of Biological Chemistry & the Center for Epigenetics and Metabolism, University of
California, Irvine, CA2
nmanik@cse.iitm.ac.in
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative condition with global health
implications. Using computational approach like differential correlation (DC) analysis1 and
MultiCens2 network analysis, we have demonstrated how these genes reshape gene correlation
networks across various brain regions in AD and highlighted the genes with high centrality,
signifying their importance. One key discovery in this study is the identification of previously
unrecognized hubs of dysregulation in AD, represented by genes like ZKSCAN1, SLC5A3, RCC1,
GPD1, PLK4, PPDPF etc. To delve into the roles of these hub genes, the research employed a
comprehensive approach that combined computational analysis and experimental methods.
MultiCens' query-set centrality approach revealed that hub genes, including SLC5A3, RCC1,
GPD1, and PLK4, are central players in gene networks related to synaptic signaling genes (SSG)
and plaque-induced genes (PIGs), emphasizing their importance in AD pathogenesis. To assess
the impact of key genes like ZKSCAN1 on microglial (HMC3 cell line) and oligodendrocyte
(HOG cell line) cell cultures, these human brain cell lines were cultured in conditioned media
collected from the transfection assay. Candidate genes were incorporated into plasmids and
transfected into HEK293 cells. Conditioned media collected from these cells were then applied to
HMC3 and HOG cell lines, leading to notable changes in the expression of Alzheimer's disease
(AD) biomarkers, including APOE, PSEN1, TREM2, and SORL1. This demonstrated the
influence of specific genes on the modulation of AD-related gene expression in these cell lines.
The research's future plans include RNA-sequencing and comprehensive bioinformatics analysis
to uncover the molecular players and pathways affected by hub genes like RCC1 and GPD1. By
using MultiCens for gene significance assessment, the study aims to reveal the mechanistic
pathways associated with these hub genes and their downstream targets identified through RNA-
Seq. In summary, this study enhances our understanding of AD's molecular basis.
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Keywords: Inter-brain-region analysis, Differential Correlation, Rewired network, Hub genes,
Conditioned Media
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Machine Learning Heuristics for Oral Cancer Datasets to Ascertain
Pathogenesis
Sai Bharath Natte1, Raja Pavan Karthik1, Sneha Kh1, Adhithya Sudeesh1 and Kavi Kishor Pb2
Amrita vishwa vidyapeetham1
Osmania university2
pbkavi@yahoo.com
Abstract
Oral cancer, an extensive and life-threatening disease, poses a significant global health challenge
due to its late-stage diagnosis and limited treatment options. Detection is critical for improving
patient outcomes and reducing mortality rates. In recent years, machine learning has emerged as a
promising tool in the field of medical diagnostics. We aimed to explore the application of machine
learning models in the detection of oral cancer, offering a novel approach to address this pressing
healthcare issue. This study presents a comprehensive investigation into the development of a
machine learning-based model for the detection of oral cancer. Creating and using a diverse dataset
of oral cancer images, our research focuses on feature extraction and classification algorithms to
identify subtle changes associated with cancerous and non-cancerous.
Keywords: Oral Cancer, Challenges, Bioinformatics, Machine Learning
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Exploration of Cannabis Constituents as Potential Inhibitors Against
Gamma-Secretase to Manage Alzheimer’s Disease: A Structural
Bioinformatics Approach
Premkumar T, Radhika G, Sajitha Lulu S*
Integrative Multiomics lab, School of Bio Sciences and Technology
Vellore Institute of Technology, Vellore – 632014, Tamil Nadu, India
ssajithalulu@vit.ac.in
Abstract
Rationale/Motivation: Alzheimer’s disease (AD) is the most common form of dementia and, is
characterized by irreversible and progressive neurodegeneration. The breakdown of Amyloid
Precursor Protein (APP) plays a crucial role in AD development and three proteolytic enzymes are
found to be critically involved in the APP breakdown process alpha-secretase, beta-secretase, and
gamma-secretase. Among these three enzymes, the gamma-secretase is highly significant as it is
involved in cleaving the APP and generating beta-amyloid plaques in the extracellular neuronal
membrane. Gamma-secretase dysfunction has been reported in AD, and several gamma-secretase
inhibitors, including natural compounds and synthetic analogs, have been developed to treat AD.
However, there is currently no treatment for AD, as most drug-like compounds have failed in
clinical trials.
Objectives: The primary objective of our study is to understand the structural behaviors of
gamma-secretase. Identification of novel inhibitors from cannabis plant sources
Methods: This study focuses on screening compounds from cannabinoid plant sources, against
gamma-secretase to identify inhibitors to managing AD. The cannabis sativa plant contains a wide
range of phytochemicals that can be used to treat various diseases. A total of 120 compounds from
cannabis plants were studied using molecular docking methods. Furthermore, we analyzed and
compared the interaction profiles of a cohort of medicinal plant compounds that have been
recognized for their neurological relevance with those of cannabis compounds. Molecular Docking
calculation was carried out using Auto dock vina and Molecular Dynamics Simulation was
performed for a time scale of 200ns for the identified complexes using CHARMM36 force field
in GROMACS. The trajectory analysis of Root Mean Square Deviation and Fluctuation
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(RMSD/F), Solvent Accessible Surface Area (SASA), Hydrogen bonds, and Radius of Gyration
(Rg) analysis was done by GROMACS and the ggplot Tidyverse package used to interpret the
results.
Results: In this study, the virtual screening was performed on a set of 120 compounds from
cannabis plants and 35 compounds from medicinal plants by using molecular docking to examine
their binding ability. The docking results revealed that screed compounds had strong binding
affinity and interactions with gamma-secretase. Notably three analgesic compounds JWH-200,
JWH-018, and JWH-166, had binding energies of -10.4 Kcal/mol, -9.9 Kcal/mol, and -9.0
Kcal/mol respectively. The control compounds including GinsenosideRd, Withanone,
withanolide_A, and GinsenosideRd had binding energies of -10 Kcal/mol, -10 Kcal/mol, and -9.4
Kcal/mol respectively. Furthermore, the 200ns molecular dynamics simulation results also
suggested that the complexes are stable throughout the simulation. According to our study, the
identified cannabinoid compounds could be a prime compound against gamma-secretase activity.
Conclusion: In conclusion, our analysis has revealed the potential of JWH-200 and
GinsenosideRd as potential inhibitors against gamma-secretase activity. The results demonstrate
both compounds had favorable binding affinities and were stable during simulation.
Keywords: Alzheimer’s disease, Drug target, cannabinoids, Molecular docking, Molecular
dynamics simulations
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An Immunoinformatic Approach to Assess the Cross Reactivity of
Indian Cobra Venom to Viper Venom
Vishnu Priya and Rani P
Department of Biotechnology, PSG College of Technology
rani.bio@psgtech.ac.in
Abstract
Rationale/ Motivation: The primary limiting step in the immunodiagnostic strip will likely be
overcome by predicting the venom’s cross-reactivity and taking measures towards it.
Objectives: To predict the cross-reactivity of Indian cobra (Naja naja) venom to viper venom by
means of common epitopes.
Background: In tropical and subtropical nations, snake envenomation is a neglected public health
issue that frequently results in life-threatening circumstances. The World Health Organization
(WHO) reports that India records the highest number of snake envenomation cases annually
roughly 81,000 with between 35,000 and 50,000 fatalities. Death due to cobra bites are mainly due
to the inefficiency of existing polyvalent anti-snake venom's (ASV). Successful antivenom therapy
depends on accurately identifying the snake species that a patient has been bitten by and the
presence of venom in their bodily fluids. The availability of an immunodiagnostic strip as a point-
of-care test would therefore aid in identifying the offending snake; but venom is a highly cross-
reactive as it is a mixture of several proteins from several families, which could lead to a false
positive during the diagnostic process. The purpose of this research is to forecast whether Indian
cobra venom cross reacts to that of vipers.
Methodology: Sequences from the NCBI were used to build the datasets for Indian cobra venom,
which contained 44 sequences from eight different protein families, and viper venom, which
contained 68 sequences from ten different protein families. The dataset's B cell, MHC I, and MHC
II epitopes were predicted using IEDB tools. The B cell epitopes were predicted using Bepipred-
2.0, Kolaskar and Tonganskar, Emini surface accessibility tools; the T cell MHC I and MHC II
epitopes were predicted using NetMHCpan 4.1 and MHC II-NP, respectively. The common
epitopes between the two snake’s datasets in each epitope group were then found using the
Interactivenn tool. Kolaskar and Tonganskar and Class I immunogenicity tools were utilized to
determine antigenic and immunogenic epitopes from the common epitopes
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Results and Discussion: Using the previously listed tools, 749 B cell epitopes, 1406 MHC I
epitopes, and 215 MHC II epitopes were predicted for the Cobra dataset, and 1125 B cell epitopes,
2256 MHC I epitopes, and 335 MHC II epitopes were predicted for the Viper dataset. The two
snake datasets were then found to share 15 B cell epitopes, 10 MHC I epitopes, and 0 MHC II
epitopes. Form the common epitopes antigenic and immunogenic epitopes were filtered out. This
resulted in four antigenic B cell and immunogenic T cell (MHC I) epitopes that belong to the
families Phospholipase A2 (PLA2), Snake venom serine-protease (SVSP), Snake venom
metalloproteinase (SVMP), cysteine-rich secretory proteins (CRISP), and L-amino acid oxidase
(LAAO). Based on the venom composition of the abovementioned families, it is determined that
cross reactivity may occur due to Phospholipase A2 (PLA2) family protein. But from the literature
it is known that three finger toxin is predominant in cobra venom which is 63.3% whereas
phospholipase is only of 11.4% contributing very less for the antibody development. So, it is
concluded that the immunodiagnostic strip developed specific for cobra venom detection may not
produce false positive results with viper venom
Conclusion: It is suggested from the results that there might not be a cross-reactivity between the
venom of cobras and vipers.
Future work: The above-mentioned methodology can be developed into a pipeline and utilized
for the prediction of cross reactivity between venoms prior to wet lab work.
Keywords: Immunoinformatic approach, cross reactivity, Indian cobra venom, viper venom
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Waste Segregation using Deep Learning Neural Network
Anjana S Nair, Lakshmi A R, Samvrutha Prasad, Sona Gokul, Joshy Alphonse and Nidheesh M,
Amrita school of biotechnology
nidheesh@am.amrita.edu
Abstract
Waste segregation is the process of categorizing waste, such as biodegradable and non-
biodegradable waste. This is critical for decreasing garbage's environmental impact since
biodegradable waste can be composted or recycled, but non-biodegradable waste must be disposed
of in a landfill. Using machine learning to classify trash photos is one method for automating waste
segregation. The goal of this research is to create a machine learning model that can categorize
garbage photos as biodegradable or non-biodegradable. A dataset of labelled waste photos will be
used to train the model. The dataset will be classified as biodegradable or non-biodegradable. Each
image in the collection will be labelled with the category to which it belongs. A number of machine
learning approaches will be used to train the model such as convolutional neural networks (CNNs).
CNNs are a sort of neural network that excels at picture classification. Once trained, the model can
be used to classify new garbage photos. To accomplish this, the model will take a garbage image
as input and estimate if the waste is biodegradable or not. The model can be used to create a waste
segregation system that can automatically classify waste photos and categorize the waste. This
technology has the potential to be employed in a variety of situations, including residences,
businesses, and waste management facilities. The model can also be used to educate people about
the importance of trash separation. The approach, for example, might be used to create a
smartphone app that allows individuals to shoot images.
Keywords: Waste Segregation, Convolutional Neural Networks (CNNs), Image Classification,
Training and Validation
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Unlocking Insights in CNS Cancer through Metabolomics and Multi-
Omics Integration
Megaswana Guruprasad and Mohanapriya Arumugam
Department of Biotechnology, Vellore Institute of technology (VIT), Vellore, Tamil Nadu, INDIA
mohanapriyaa@vit.ac.in
Abstract
Central nervous system (CNS) cancers, while rare, impose a significant burden on patients and
have high mortality rates. The scarcity of data historically associated with these malignancies has
driven innovative approaches to maximise information from diagnostic tests, leading to the
adoption of high-throughput technologies, including metabolomics in the field of systems biology.
Metabolomics focuses on profiling small-molecule biochemicals (metabolites) within biological
systems, offering insights into the complex metabolic alterations in neuro-oncology. It plays a vital
role in identifying potential quantitative metabolic biomarkers for early cancer detection and
evaluating treatment effectiveness.
The journey to discover these biomarkers begins in a preclinical setting, involving the use of
animal models and human cell cultures, such as the glioblastoma-derived U87MG cell line, to
identify candidate biomarkers. Subsequent translational validation efforts ensure their clinical
relevance by confirming them in biofluids or tumour tissues. Neuro-oncology is inherently
multidisciplinary, spanning domains like imaging, histology, and molecular data, including
genomics, epigenomics, proteomics, and metabolomics. Techniques such as nuclear magnetic
resonance spectroscopy (NMR) and mass spectrometry (MS) are powerful tools for characterising
the complex metabolic landscape in neuro-oncology.
A major advancement in neuro-oncology research is the consolidation of big data resources
through open-access initiatives, enabling integrated multi-omics analysis to discover novel
biomarkers and therapeutic interventions. Metabolomics, in conjunction with other "omic"
disciplines, deepens our understanding of neuro-oncology, offering prospects for improved
diagnosis, treatment, and patient outcomes. Metabolomic databases and tools like MetaboAnalyst
and the Human Metabolome Database (HMDB) are instrumental in metabolomics research,
facilitating data analysis and interpretation. For brain cancer research, oncology data sources
include repositories of brain cancer patient data, tissue samples, and cell lines, such as the Cancer
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Genome Atlas (TCGA) and the Human Glioblastoma Cell Atlas. These resources contribute to a
comprehensive understanding of the molecular and metabolic complexities in CNS cancers.
Keywords: Metabolomics, Central nervous system (CNS) cancers, Neuro-oncology, Biomarkers,
Multi-omics analysis, Nuclear magnetic resonance spectroscopy (NMR), Mass spectrometry (MS)
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Comparative Atomistic Insights on Apo and ATP-I1171N/S/T in Non-
Small-Cell Lung Cancer
Ambritha Balasundaram and George Priya C Doss
Laboratory of Integrative Genomics, Department of Integrative Biology, SBST, VIT
georgepriyadoss@vit.ac.in
Abstract
Anaplastic lymphoma kinase (ALK) rearrangements occur in about 5% of non-small cell lung
cancer (NSCLC) patients. Despite being first recognized as EML4-ALK, fusions with several
additional genes have been identified, all of which cause constitutive activation of the ALK kinase
and subsequently lead to tumor development. ALK inhibitors first-line crizotinib, second-line
ceritinib, and alectinib are effective against NSCLC patients with these rearrangements. Patients
progressing on crizotinib had various mutations in the ALK kinase domain. ALK fusion proteins
are activated by oligomerization through the fusion partner, which leads to autophosphorylation
of the kinase’s domain and consequent downstream activation. The proposed computational study
focuses on understanding the activation mechanism of ALK and ATP binding of wild-type (WT)
and I1171N/S/T mutations. We analyzed the conformational change of ALK I1171N/S/T
mutations and ATP binding using molecular docking and molecular dynamics simulation (MDS)
approach. According to Principal component analysis (PCA) and Free energy landscape (FEL), it
is clear that I1171N/S/T mutations in Apo and ATP showed different energy minima/unstable
structures than WT-Apo. The results revealed that I1171N/S/T mutations and ATP binding
significantly supported a change toward an active state conformation, whereas WT-Apo remained
inactive. We demonstrated that I1171N/S/T mutations are persistent in an active state and
independent of ATP. The I1171S/T mutations showed greater intermolecular H-bonds with ATP
than WT-ATP. The molecular mechanics poisson-boltzmann surface area (MM/PBSA) analysis
revealed that I1171N/S/T mutations binding energy were similar to the WT-ATP. This study
shows that I1171N/S/T can form stable bonds with ATP and may contribute to constitutively active
kinase. Based on the Y1278-C1097 H-bond and E1167-K1150 salt bridge interaction, I1171N
strongly promotes constitutively active kinase independent of ATP. This structural mechanism
study will aid in understanding the oncogenic activity of ALK and the basis for improving the
ALK inhibitors.
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Keywords: Anaplastic lymphoma kinase, ATP, NSCLC, Mutation, I1171X, oncogenic activation
Indian Conference on Bioinformatics 2023 - Inbix'23
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L. acidophilus Mv Hampers the Gtfb Enzyme and Impedes the
Formation of Dental Plaque by S. Mutans: An In Vitro and In Silico
Approach
Venkatramanan M and Nalini Easwaran,
Vellore Institute of Technology
nalini.e@vit.ac.in
Abstract
Rationale: Oral infections mediated by Streptococcus mutans pose a serious challenge to human
health across the globe. This opportunistic pathogen is implicated in aggressive periodontitis and
leads to various systemic disorders, which result in a significant socioeconomic burden. The
conventional treatment of oral infections with antibiotics has led to the emergence of resistant
strains due to selective pressure. Furthermore, the efficacy of synthetic chemicals as antimicrobials
may decline over time due to inconsistent usage. Hence, natural agents may provide a superior
alternative. We aim to investigate the efficacy of membrane vesicles (MV) derived from
Lactobacillus acidophilus, a commensal microorganism of the human gut and oral niche, to inhibit
S. mutans infection. Membrane vesicles are nanosized spherical entities that originate from the
outer membrane. They harbor antimicrobial peptides (Bacteriocin), protein, DNA and RNA. The
antimicrobial peptides in membrane vesicles may exhibit a narrow or broad spectrum of activity,
which could be harnessed for innovative therapeutic approaches.
Materials and methods: We isolated and characterized L. acidophilus membrane vesicles using
a system bioscience kit, DLS, NTA and FESEM. We assessed the impact of membrane vesicles
on S. mutans viability, EPS production and biofilm formation in vitro. We performed homology
modelling of bacteriocin using the Swiss model online tool and targeted it against gtfB, a key
protein involved in biofilm formation by S. mutans, using molecular docking and dynamics
approach. We confirmed the modulation of gtfB expression by membrane vesicles using RTPCR
in vitro.
Results and discussion: L. acidophilus, a commensal bacterium of the human gut, possesses
immunomodulatory, antitumor and antimicrobial properties. However, the role of its membrane
vesicles (MVs), which are nanoscale spherical structures released from the bacterial membrane,
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remains elusive. In this study, we aimed to isolate and characterize MVs from L. acidophilus and
investigate their antimicrobial potential against S. mutans, a major causative agent of dental caries.
We employed Dynamic Light Scattering (DLS) and Nanoparticle Tracking Analysis (NTA) to
determine the size distribution and particle concentration of the MVs, which ranged from 100 to
300 nm and reached 2.7 X 109 particles, respectively. We also visualized the morphology of the
MVs by FESEM, which revealed spherical structures with smooth surfaces. We then assessed the
antimicrobial and antibiofilm efficacy of the MVs against S. mutans in vitro using plate and broth
assays. The MVs exhibited remarkable activity against S. mutans, which was abolished by
proteinase K treatment, indicating that bacteriocins, which are antimicrobial peptides, were
responsible for the activity. To identify the bacteriocins involved, we retrieved their sequences
from L. acidophilus based on a previous study by Dean et al and performed molecular modelling
and Ramachandran plot validation to predict their structures. We also analyzed their
physicochemical properties and toxicity using DBAASP and Toxinpred online tools. We selected
gtfB as the target protein of S. mutans from string analysis, as it is a key virulence factor that
mediates adhesion, aggregation and biofilm formation of S. mutans in the oral environment. We
conducted molecular docking of the bacteriocins with GtfB using Haddock online server and found
that Lactacin B had the highest affinity for GtfB with a binding energy of -15.8 kcal/mol. We
validated the interaction stability by GROMACS simulation. We also performed RT PCR to
measure the expression of gtfB in S. mutans treated with MVs and found that it was significantly
downregulated. These findings demonstrate that L. acidophilus MVs exert antimicrobial and
antibiofilm effects by modulating gtfB expression in S. mutans.
Conclusion: The potential of membrane vesicles (MVs) derived from Lactobacillus acidophilus
as a novel therapeutic strategy against oral pathogens is demonstrated in this study. This approach
could help mitigate the adverse impacts of oral diseases on human health and well-being, as well
as the economic costs associated with their treatment. The antimicrobial activity of the peptides
contained in the MVs was not directly assessed in this work, but it will be investigated in future
experiments.
Keywords: Membrane vesicles, Oral pathogen, In silico, Biofilms
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Exploring Different Traits of the Isolate Priestia megaterium VIT For
Maintaining Equilibrium in Energy Expenditure Between Systemic
Resistance and Plant Growth
Huldah Lazarus and Nalini Easwaran, Vellore Institute of Technology
nalini.e@vit.ac.in
Abstract
Rationale: Population growth leads to higher demand for food crops, which also face more disease
challenges. However, the technologies that enhance crop yield also make them more vulnerable to
various biotic and abiotic stresses, especially diseases. Plants have to balance their energy
allocation between growth and immunity when they encounter pathogens, resulting in trade-offs.
Therefore, using biocontrol agents may help plants to improve both growth and immunity and
contribute to sustainable development. PGPR (Priestia megaterium) is a promising biocontrol
agent that can provide plant health protection through different mechanisms, such as quorum
quenching, antimicrobial secondary metabolite production, and osmoprotectant production. The
PGP-related genes can enhance plant growth and health. Moreover, the genes encoding for
bacteriocin and rhizobactin can prevent phytopathogenic infections. Additionally, the presence of
heat and cold shock proteins allows them to survive in harsh environments and assist plants in
disease prevention and growth promotion. Therefore, exploiting Priestia megaterium can have dual
benefits: one is by boosting plant growth and the other is by inhibiting phytopathogenic infections.
Materials and Methods: To assess the PGPR potential of isolated strains, we performed
biochemical assays to measure their antibiotic and antimicrobial resistance, salt and pH tolerance,
temperature range, and phosphate solubilization capacity. We sequenced the genomic DNA of the
strains using the Illumina platform with 150 bp paired-end reads. We assembled the reads using
SPAdes software and validated the assembly using barrnap version 0.9. We completed the genome
using the MeDuSa web-based tool.
Results and Discussion: Biochemical tests identified one of the isolated bacteria from biofertilizer
as PGPR. The strain was a Gram-positive, rod-shaped bacterium with glossy white, entire, and
umbonate colonies. It showed tolerance to a pH range of 3-10, temperature range of 20-40, and
salt range of 2-8%. It was resistant to ampicillin but sensitive to other antibiotics tested. It also
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exhibited anti-microbial activity against the plant pathogen P. syringae. Whole genome sequencing
of the isolate using the Illumina platform showed 99% similarity with Priestia megaterium. The
whole genome sequencing revealed a total read length of 5,415,392 bp and an N50 value of
431,205. The GC content was 38% and the genome size was within the reported range. Several
genes related to plant growth promotion and anti-microbial properties were predicted in the
genome, such as glucose dehydrogenase Zwf, trehalose metabolism genes like treP,C,R, heat
shock proteins GroeS, GroeL, cold shock proteins CspA,B,D,E, glycine-betaine OpuD, thiol
peroxidases Bcp-1,2, glutathione peroxidase BsaA, catalases CotJC,E,H,JB,NE,R,S, SodAC,
GABA production GabP,D. Glycine-betaine is an osmoprotectant that helps plants cope with
abiotic stresses by osmoregulation. Trehalose is another stress protectant and the heat and cold
shock proteins help bacteria survive in harsh environments. Peroxidases protect bacterial cells
from oxidative and osmotic stresses. Bacteriocins and rhizobactins identified through sequencing
are antimicrobial peptides and siderophores, respectively, encoded by genes that have been widely
reported to inhibit bacterial growth by competing for essential nutrients. Thus, both growth as well
as disease control can be achieved through this strain P. megaterium VIT.
Conclusion: Current research aimed to achieve sustainable development in the face of increasing
disease severity and crop loss due to population growth. There is a global shift in the agricultural
paradigm towards sustainability. Current research goal considers disease control and growth
promotion as interrelated aspects of plant health. WGS analysis of the isolate identified genes that
enabled the isolate to adapt to harsh conditions and enhance plant health by phosphate
solubilization, siderophores production, osmoprotection, stress tolerance and phytopathogen
inhibition by anti-microbial activity.
Keywords: Priestia megaterium, WGS, PGPR, sustainable development
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Assessment of Anti-inflammatory Potentials of Rosa indica
Santhi N, Sahana Prajwala M, Raja Dharani A and Nithya S
Avinashilingam institute for Home Science and Higher Education for Women
23pbi002@avinuty.ac.in
Abstract
Inflammation is a multifaceted process implicated in numerous pathologies, including male
infertility, a condition affecting couples worldwide. Rosa indica, known for its traditional
medicinal use, has garnered interest for its potential anti-inflammatory and reproductive health
benefits. In this study we examined the anti-inflammatory and antioxidant properties of fresh and
dried Rosa indica petals by analysing 20 to 100 µl of 1 mg/ml concentration of extract using
albumin denaturation and DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) assays. The extracts
were characterized using GC-MS analysis to identify their bioactive constituents. To evaluate the
efficiency of the extracts, Schrodinger LLC., Software (GLIDE SP and GLIDE XP) were
employed and validated using MM-GBSA module. The ethanolic extract exhibited significantly
high anti-oxidant activity (83.18±0.03 and 90.63±0.02 %) and anti-inflammatory effects
(59.96±0.49 and 57.27±0.16 %) for fresh and dry petals respectively at p-vlaue <0.001. Molecular
docking studies identified Kaempferol as a top hit compound with a docking score of -9.048 for
cyclooxygenase 2; -9.226 for androgen receptor; and -5.56 for AKT1. The compound 2,4-DTBP
showed the better docking score of -6.379 than Kaempferol with -4.846. Least binding energy
was recorded for the best docked compound Kaempferol. Molecular Dynamics simulation studies
were further performed to see the stability of the complex structure.
This research offers insights into the molecular interactions of Rosa indica extracts with
inflammatory proteins and the target proteins involved in the process of steroidogenesis shedding
light on potential mechanisms for their properties. These findings not only support the traditional
use of Rosa indica in herbal medicine but also lay the groundwork for further experimental
validation and the development of novel anti-inflammatory interventions. Furthermore, this study
underscores the utility of computational methods in the initial screening of natural compounds,
potentially accelerating the discovery of therapeutic agents for male infertility and related
conditions. The discoveries made in this work provide a promising avenue for advancing clinical
research and therapeutic development.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Keywords: Rosa indica, Antioxidant, Male infertility
Indian Conference on Bioinformatics 2023 - Inbix'23
164
Transcriptomic Analysis Reveal Tissue-Specific Gene Regulatory
Circuits Associated with Systemic Lupus Erythematosus: A Systems
and Computational Study
Blessy Kiruba, Akshayata Naidu, Sajitha Lulu S and Vino Sundararajan, Vellore Institute of
Technology,
ssajithalulu@vit.ac.in
Abstract
Rationale: Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized
by immune system dysregulation, leading to widespread inflammation in multiple organs. Loss of
immune tolerance against auto-antigens that are produced by poor clearance of apoptotic bodies
remains causes tissue damage. Molecular intricacies involved in the process are not fully
characterized and need further elucidation for the development of more effective and precise
treatment options.
Objective: To deduce gene regulatory circuits associated with the pathophysiology of SLE using
RNASeq data from PBMC samples. To deduce gene regulatory circuits associated with the
pathophysiology of SLE using RNASeq data from specific cells/tissues (Neutrophils, Dendritic
cells, Macrophages). To construct a molecular atlas of inter and intra-cellular gene regulatory
circuits involved in SLE for identification of molecular mediators and pathways as therapeutic
targets
Methods: Differentially expressed genes (DEGs) were retrieved from gene expression datasets
derived from PBMC samples, Neutrophils, Dendritic cells and Macrophages
A curated list of genes was used as input for the construction of Protein-Protein Interaction (PPI)
network and topological network analysis was performed for the identification of Hub Genes
Network Modules. Associations between identified hub genes/modules were further characterized
using corelation, clustering and regression analysis using an integrated dataset of SLE patients
(>120 samples) for the establishment of gene regulatory circuits. The generated circuits were
integrated and compiled and validated from literature for the construction of a molecular atlas
associated with the pathophysiology of SLE.
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Results: Several genes induced by interferons (IRF7, IFI35, IFT1, OAS2, etc.) were identified as
hub genes along with genes involved in DNA-damage (PARP9, PARP14). Hierarchical clustering
identifies several gene clusters associated with the pathophysiology of SLE, prominent among
which were clusters with interferon induced mediators and with microRNAs involved in RNA
silencing (MIR553, MIR3173, MIR644A, MIR199A1, etc). Hereafter regression analysis revealed
key gene regulatory circuits associated with the regulation of interferon mediated signalling
pathways involved in the pathophysiology of SLE in PBMC samples and in different leukocytes.
Conclusion: Systems and Computational analysis of transcriptomics dataset reveal prominent
gene regulatory circuits and key regulators associated with the molecular pathogenesis of SLE
Keywords: Non-coding RNA, Systemic Lupus Erythematosus, Tissue-specific gene expression,
RNASeq data, Clustering, Regression
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In Silico Screening of Phytochemicals from Indian Medicinal Plants for
the Identification of Potential Antibacterial Activity Against
Mycoplasma Penetrans
Kandavelmani Angamuthu, Princy Caron, Sanjana Sunil and Surega Madhavan,
Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam university,
Coimbatore, India
kandavelmani_bc@avinuty.ac.in
Abstract
Mycoplasma penetrans, a species of Mycoplasmataceae, is a free-living, gram-positive bacterium
having a reduced genome size of 1.3Mb. M.penetrans is a primary pathogen responsible for urinary
tract infections and breathing disorders. Current treatment method involves the use of antibiotics
and antiviral drugs to prevent the disease progress. Due to the increasing mutation rates, many
microorganisms have obtained antimicrobial resistance. To prevent this ,we identified
phytocompounds from 20 Indian medicinal plants which can be used to combat the activity of
M.penetrans. Several attempts have been made to investigate Indian medicinal plants recognised
for their health benefits and immune-boosting properties as well as to explore the possibility of
repurposing current medications known for their antibacterial activities. The current study is
focused on insilico screening of phytochemical compounds against carbamate kinase and arginine
deiminases. Research studies have identified carbamate kinase and arginine deiminase enzymes of
M. penetrans as potential drug targets. In addition to their functional significance, these enzymes
have also been found to possess high chemical and proteolytic stability. In the present study, we
have selected 114 phytocompounds from numerous Indian medicinal plants. These
phytocompounds were subject to a series of systematic bioinformatics analyses. Based on the
combined results of these analyses, a set of 59 phytocompounds were shortlisted. Inhibitory
activity of these compounds against carbamate kinase and arginine deiminases was studied using
virtual screening approach. Phytocompounds with highest binding efficiencies were then
identified. These compounds could be used as promising lead molecules for the development of
novel antibiotics to combat infections caused by M. penetrans.
Indian Conference on Bioinformatics 2023 - Inbix'23
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Keywords: M Penetrans, Urinary infections, Antibacterial activity.
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Comparative Analysis of Physicochemical Features of Structured and
Disordered Proteins in Humans
Prachi Bhargava, Bishal Samanta and Amita Barik
National Institute of Technology, Durgapur
amita.barik@bt.nitdgp.ac.in
Abstract
Proteins play a crucial role in a myriad of biological processes ranging from enzymatic catalysis
to structural support, underscoring their significance in the living world. The present study focuses
on the comparative analysis of two distinct classes of proteins: structured and intrinsically
disordered proteins (IDPs) present in humans. IDPs lack a stable three-dimensional structure and
possess high conformational flexibility, serving as critical hubs in protein interactions. They are
involved in various cellular processes and in the onset and progression of diseases such as cancer,
neurodegenerative disorders, and cardiovascular conditions. The study employs a non-redundant
dataset of 1090 disordered proteins containing 1637 intrinsically disordered regions (IDRs) of
Homo sapiens curated from DisProt database. The length of the IDPs in our dataset varies from 24
residues to 34,350 residues while the IDR length range from 9 to 2152 amino acids. We find that
the IDRs are enriched in polar and charged residues (29%) and depleted in aromatic amino acids
(5%) with tryptophan being the least (0.7%). Structured polypeptide chains curated from UniProt
on the other hand have higher content of aromatic residues (8%) and have relatively low polar
charged residues (23%). Cysteine content, however is more in case of structured regions as
compared to the disordered regions. Overall content of non-polar residues in IDRs are less (40%)
as compared to structured proteins (42%), however, proline (8.3%) and glycine (7.9%) are more
preferred in IDRs than in the structured proteins (Pro 5.8%, Gly 6.4%). Also, the contribution of
individual residues is not dependent on the length of IDRs. This comparative analysis highlights
notable distinctions in the behaviours of structured and disordered proteins, unveiling factors that
govern their sequence-structure-function relationships. These findings offer valuable insights into
the complexity of cellular processes and developing therapeutic strategies for diseases involving
protein misfolding and dysfunction.
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Identification of the Common Differentiating Genes Among Various
Cancers on the Basis of RNA Expression
Krishna Sayantika Deb, Kuntal Pal and Rahul Shaw
Vellore Institute of Technology
kuntal.pal@vit.ac.in
Abstract
Various types of cancer prevalence worldwide make it the second biggest killer disease. The gene
expression analysis was conducted from the public expression profile data of five cancer cells
(breast, lung, glioma, cervical and Thyroid cancers). The comparative RNA-seq data analysis of
expressing and regulatory genes in cancer cells may provide a detailed insight into their role in
cancer metabolism. The study was done to identify the common upregulated and downregulated
genes and their pathways among multiple types of cancers, based on their gene expression profile.
The gene expression quantification from multiple RNA-seq data obtained from Gene Expression
Omnibus (GEO) for Human was performed using Salmon. Thereafter, Differential Gene
Expression (DEG) analysis among multiple cancers done by edgeR DEG analysis pipeline in R.
The comparative gene expression analysis identifies common up-regulated and down-regulated
genes among five cancers. The differential expression patterns of various genes among these five
cancers show PRSS23 and LINC commonly up-regulated in glioma, breast and lung cancers.
PSR23, belongs to serine protease family via estrogen receptor pathway regulates the proliferation
of cells. The commonly down-regulated genes are SNHG and LINC, among breast, lung, and
thyroid cancers. The common up-regulated and down-regulated genes can be further explored as
common potential biomarkers. The DEG analysis for regulatory genes in five cancers was further
explored by STRING analysis and the KEGG pathway for their role in the progression of cancers
while regulating various metabolic pathways involved in cancer metabolism.
Keywords: cancer, gene expression, RNA-seq data, cancer metabolism, upregulated
downregulated, Gene Expression Omnibus, edgeR DEG analysis
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In Silico Analysis of Atropine as a Potential Inhibitor of NS5 Protein of
Japanese encephalitis Virus
Toufeeq Ahmed A1, Priya V1, Swagatika Priyadarsini2, Faraz Ahmad1 and Mohanapriya
Arumugam1
Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of
Technology, Vellore, India1,
Scientist, Biochemistry unit, ICAR- National Research Centre on Camel, Rajasthan2.
faraz.ahmad@vit.ac.in
Abstract
The most common viral encephalitis in the world is caused by Japanese encephalitis virus (JEV),
which spreads through mosquito bites. This fatal brain infection occurs at an average of 70000
cases annually. In spite of the efforts made to identify and select several targets which are essential
in progression of JE, there is yet no licensed drug available against this deadly pathogen as it has
a high mutation rate. Therefore, it is necessary to find a suitable drug against JEV, and this can be
done by inhibiting one of its target molecules non-structural protein 5 (NS5), which will inhibit
the replication of JEV, as the NS5 protein is a multi-enzymatic protein that plays a vital role in
viral RNA replication. NS5, like other flavivirus non-structural and cellular proteins, has a methyl
transferase domain in the N-terminus and an RNA-dependent RNA polymerase (RdRp) domain in
the C-terminus. The NS5 methyltransferase domain is mostly responsible for viral genomic RNA
5′ capping, whereas the RdRp domain directly participates in RNA replication. Although the
antiviral properties of hyoscyamine and scopolamine, the two active compounds from Atropa
belladonna have been studied in JEV, the effect of its third active component, atropine, has not
been investigated yet. In this study, we observed that atropine binds with NS5 protein of JEV with
a binding energy of -7.00 (kcal/mol) which is similar with that of the positive control curcumin (-
7.84 kcal/mol). Though in silico docking and simulation suggests that atropine could be a
promising therapeutic option against JEV replication, further in vitro and in vivo experiments are
to be carried out in the future course of time to validate the current results.
Keywords: Japanese encephalitis virus, NS5, natural compounds, molecular docking and
simulation, antiviral
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Computational Analysis of Structure and Function of Siglec1 and
Prediction of Potential Pharmaceutical Agents for Rheumatoid Arthritis
Lakshmi K and Vino S,
Vellore Institute of Technology
svino@vit.ac.in
Abstract
Siglec1 (Sialoadhesin/CD169) is a novel sialic acid-binding Ig superfamily type 1 membrane
protein, a molecule that binds glycoconjugate ligands on cell surfaces in an alpha (2, 3)-linked
sialic acid-dependent manner. This protein is involved in mediating cell-cell interactions, is only
expressed by a subpopulation of macrophages, and plays an important role in autoimmune disease
and rheumatoid arthritis (RA). This study aims to find a potential natural inhibitor for the siglec1
biomarker. We can better understand the function of this class of proteins by using computational
techniques to predict the structure and binding location of siglec1. We used the computerized
method of protein modeling and small molecules collected from different sources, structure-based
virtual screening, ADMET property predictions, molecular docking, and molecular dynamic
simulation studies to screen potential siglec1 inhibitors. Rheumatoid arthritis has been effectively
and safely treated using herbal therapies, which have low toxicity and fewer side effects. A total
of 1600 natural compounds were collected from 150 anti-inflammatory and anti-RA medicinal
plants from the literature and the supernatural database 3.0 was used for extracting natural
compounds and was used for molecular docking, which evaluates the binding free energy and
binding affinity of H-bond interaction score-based 10 hit list compounds. Re-docking with a 10-
hit list and Type-A Procyanidins has the highest binding affinity (-8.5 Kcal/mol) to siglec1. The
configuration stability of siglec1 and the ligand complex was predicted by 100 ns molecular
dynamic simulation (MDs) and conformed complex stability. Our study's final outline was found
to be a novel natural potential inhibitor against the target siglec1.
Keywords: Siglec1, Docking, ADMET, Modeling, Molecular dynamic simulation (MDs),
Rheumatoid arthritis (RA)
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Transcriptomic Analysis of Stress Response Pathway in HEK293 Cells
During Recombinant AAV Production
Anju Ganesan and Balaji Balakrishnan
Department of Integrative Biology, SBST, Vellore Institute of Technology, Vellore, Tamil Nadu.
balaji.balakrishnan@vit.ac.in
Abstract
Background: Recombinant Adeno-associated viral (AAV) vectors have been successfully used
in gene therapy to treat rare disorders such as spinal muscular dystrophy, hemophilia A and
hemophilia B etc. However, optimizing the productivity and vector quality of rAAV is essential
to meet the increasing clinical and commercial demand for gene therapies. Recombinant AAV
vectors are packaged by standard triple transfection methods in producer cell lines. Here we
hypothesize that during rAAV production, the producer cell lines are subjected to huge
endoplasmic reticulated stress which ultimately impacts vector production qualitatively and
quantitatively.
Aim: To evaluate cellular stress response during recombinant Adeno-associated viral vectors
(rAAV) production in HEK293 cells.
Methods: Raw sequencing data (GSE224405) in the form of sequence read archives (SRA) files
were retrieved using galaxy, FASTQ files were then subjected to a quality check using FastQC.
The reads were then mapped to the reference human genome using Kallisto. Normalization and
differential expression analysis was carried out in R using edgeR package and limma package. The
interactions of DEGs and the related partners were curated from the STRING and visualised using
Cytoscape. The DEGs subjected to Gene enrichment analysis. qPCR was performed to validate
the differentially expressed genes.
Results and Conclusion: We analysed 145 significant DEGs (p-value < 0.05; fold 2 change ≥ 2
or ≤ -2). The Gene ontology analysis of DEGs were categorised for stress response pathways,
which determined the involvement of Unfolded Protein Response (UPR) and DNA Damage
Response (DDR) pathways. Interestingly, STRING analysis shows that DDIT3 (induces cell cycle
arrest and apoptosis) followed by HIF1A (transcriptional regulator of the adaptive response to
hypoxia) protein had direct interaction with XBP1 protein. Among the DEGs analysed 31
Unfolded Protein Response genes and 7 DNA Damage Response genes were differentially
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expressed. UPR genes such as IRE1α, XBP1 and HSPA9 showed a significant expression.
Whereas, UPR pathway could be a significant stress response pathway impeading for the viral
vector production in HEK293 cells because during production host cell protein machinery is used
and ongoing validation work showed the involvement of UPR gene (IRE1α) pathway. Therefore,
HEK293 cells will be subjected with UPR which will be a greater impact for rAAV production,
accordingly UPR pathway can be regulated in HEK293 cells for improved rAAV production.
Keywords: Differential gene expression analysis, DEGs, Unfolded Protein Response, rAAV
production
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174
Single Nuclei Transcriptomics Datasets of Brain Tissue Analysis by In-
House Integrated Cross Species Platform
Abhishek Pandey1, Aravind Easwar1, Debasmita Paul2, Rakshika Raveendran3 and Manikandan
Narayanan1
Computer Science and Engineering, Indian Institute of Technology Madras1
Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras2
Electrical Engineering, Indian Institute of Technology Madras3
nmanik@cse.iitm.ac.in
Abstract
Various computational methods have been developed over the last decade to handle the rapidly
expanding single cell transcriptomics data. However, there is lack of unified platform for the
biologists to use for analysis of the data sets. We, in our facility, have developed and are using an
in-house constructed integrated pipeline (presented in INCOB23 conference) for analysis of cross
species single nuclei datasets in our pursuit to understand the brain evolution of various animal
groups. We have isolated nuclei from flash frozen brain tissue and using 10X genomics
technology, we generated single nuclei cDNA libraries. It was sequenced with depth of 120 million
reads and we obtained FastQ files as output. These files were put through the pipeline which is
built based on Cell Ranger v7.1 software, Seurat 4.1.1, and Clustermole 1.1.0 packages in a unified
interface that supports cell-type labelling for multiple different species. As output we obtained
barcode rank plots, gene count matrix and operations like normalization, dimensionality reduction
(PCA, tSNE, UMAP) and clustering and cell type labels using differentially expressed genes. We
also obtained various quality control plots like violin, elbow and jackstraw plots. This study is
preliminary in nature and the datasets are obtained in-house and then passed through the integrated
platform to assess the functionality of the pipeline. These operations are performed and results are
made available to the user for download. In future, we aim to produce more single nuclei datasets
and create gene-gene homology maps and lineage trees between various species. Our interface acts
as a bridge for a biologist to overcome the hurdles of prior computational expertise in using these
tools individually. The code is available at: https://github.com/BIRDSgroup/mdn.
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Keywords: integrated pipeline single, nuclei RNA sequencing data, Cell ranger, Seurat,
clustermole, UMAP, tSNE
Indian Conference on Bioinformatics 2023 - Inbix'23
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Investigation of Phe-tRNA interaction with EF-Tu in GDP/GTP Nucleotide
bound states: A molecular dynamics simulation study
Kavya Kallahalli Mohan Kumar1, Upendra Nagarajachari2, Shuchika Devaraj Biligere3 and
Krishnaveni Sannathammegowda4.
Department of Studies in Physics, University of Mysore1,
Regional Institute of Education, Mysuru2,
Department of Studies in Physics, University of Mysore3,
Department of Studies in Physics, University of Mysore4
sk@physics.uni-mysore.ac.in
Abstract
Elongation factor Tu (EF-Tu) is an important class of translational GTPases (trGTPases) involved
in the elongation process. EF-Tu functions by forming the tight complex with the aminoacyl-tRNA
(aa-tRNA: transfer RNA carrying an amino acid) in GTP-bound active state and subsequently
transports the aa-tRNA to the A-site (aminoacyl-site) of the ribosome. The correct interaction
between mRNA's codon and aa-tRNA anticodon triggers GTP hydrolysis, resulting in the
transition of EF-Tu from the active state to the inactive state (EF-Tu bound to GDP). This, in turn,
results in the dissociation of EF-Tu:GDP from the tRNA and ribosome. However, the
conformational changes and interactions responsible for the tight complex formation between EF-
Tu and aa-tRNA in the GTP-bound state and the disruption of the interaction between EF-Tu and
aa-tRNA in the GDP-bound state are not well understood. Therefore, to explore the conformational
changes in the EF-Tu:Phe-tRNA complex and to get insight into the interaction between Phe-
tRNA (tRNA carrying Phenylalanine) and EF-Tu in GDP/GTP nucleotide bound state, 200 ns MD
(molecular dynamics) simulation have been carried out. The RMSD, RMSF, cluster, and DSSP
analyses suggest that the GTP-bound state attains a more favorable conformation, which may
facilitate EF-Tu’s interactions with the tRNA and ribosome. Further investigation of the non-
bonded interaction energy calculation revealed the significance of domainII in interaction with the
Phe-tRNA in a GTP-bound state. In addition, the H-bonds calculated between the tRNA’s Phe
(Phenylalanine attached to the tRNA is considered) and domainII highlights the contribution of
Val285 in recognizing tRNA’s Phe by forming an H-bond throughout the simulation time in the
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GTP bound state. Overall, these results provide insight into how GTP nucleotide influences the
interaction between EF-Tu and Phe-tRNA.
Keywords: Molecular dynamics simulation, GTPases, EF-Tu, tRNA, Ribosome
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Comprehensive Analysis of Amyotrophic Lateral Sclerosis Gene
Expression Data to Ascertain Candidate Biomarkers
Deboral E and Mohana Priya A,
Vellore Institute of Technology, Vellore
mohanapriyaa@vit.ac.in
Abstract
Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disease characterized by the
progressive degeneration of motor neurons, leading to muscle weakness, paralysis, and respiratory
failure. Understanding the molecular mechanisms underlying ALS pathogenesis is crucial for the
development of effective therapeutic strategies. In this study, we conducted a comprehensive
analysis to identify key genes involved in ALS pathology. Through differential gene expression
analysis of the GEO dataset GSE3307, we identified a set of up-regulated and down-regulated
genes in ALS. Among the up-regulated genes, CTNNB1, EP300, PIK3R1, EGFR, ESR1, RHOA,
CDC42, MAPK14, and MDM2, along with the down-regulated genes UBB and UCC, emerged as
important candidate genes implicated in ALS. These genes have been associated with various
cellular processes and pathways relevant to ALS, such as Wnt signaling, transcriptional regulation,
PI3K signaling, neuronal survival, inflammation, and protein homeostasis. To elucidate the
interactions and functional relationships among these genes, we constructed a protein-protein
interaction (PPI) network using the STRING database. The PPI network analysis revealed
significant clustering and identified hub genes, including CDC42, MDM2, and RHOA, which play
crucial roles in ALS pathology.
Keywords: amyotrophic lateral sclerosis, gene expression, protein-protein interaction network,
hub genes, pathway enrichment analysis
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Isolation, Characterization, and Genome Analysis of Lytic
Bacteriophage Vb_SalP_1 against Food-borne Pathogen
Salmonella
enterica
Ramya Juliet, Oishi Mitra, Bershiyal S and Ramesh Nachimuthu
Vellore Institute of technology, Vellore, Tamil Nadu
ramesh.n@vit.ac.in
Abstract
Salmonella causes gastrointestinal infections and is widespread in developing countries like India
due to poor and unhygienic environments. The development of fluoroquinolone resistance and
multi-drug resistance in Salmonella has been a major concern. Having more than 2600 serovars in
Salmonella species it is important to find a targeted solution for each serovar when antibiotic
treatment fails. Phage therapy comes to the rescue as an alternative therapy. In this study,
vB_SalP_1 phage was isolated from a sewage sample, and tested for its host range, morphological
characteristics, stability in different temperatures and pH, life cycle, and phage-inhibition assay.
Since Salmonella is a food-borne pathogen, its stability and lytic activity in food sources like egg
was also determined. vB_SalP_1 had a broad host range killing up to 80% (n=10) of tested isolates,
with high tolerance to temperatures and pH, showing maximum lytic activity in vitro in all MOIs
from 100 to 0.0001 up to 6 h. 95% of the phages got adsorbed on its host in 3 minutes with a latent
period of 5 minutes and a burst size of 50 phage particles per host cell. To determine the stability
in the food source, the phage (1010) was incubated with egg white and egg yolk separately for 1 h
and observed that the phages remained more stable in egg white than egg yolk and were able to
inhibit bacterial growth in egg white effectively. TEM analysis revealed the morphology as a T7-
like Podoviridae phage from Caudoviricetes order with a head size of 45±5.0 nm and a short tail.
vB_SalP_1 consists of double-stranded DNA with 37,099 bp with a G+C content of 51.12%, with
predicted 44 open reading frames by PHASTER and with no tRNAs found by ARAGORN. Blastn
revealed the closest similarity with Salmonella phage ST38 (OQ860974.1; query coverage, 98%)
and likely belongs to Autographiviridae; Studiervirinae; Kayfunavirus based on preliminary
analysis. Therefore, the isolated phage can be effectively used as a promising agent to prevent and
control Salmonella infections.
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Keywords: Salmonella, food biocontrol, fluoroquinolone, resistance, bacteriophages vB_SalP_1,
phage therapy, Salmonella genome
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Exploring Salidroside and its Derivative Compounds as Potential
DAPK1 Inhibitors: An In Silico Study for Reducing Alzheimer's Disease
Risk
Tanish Prashar, Shreshtha Bhattacharya, Pritha Saha and Priti Talwar
Vellore Institute Technology
priti.t@vit.ac.in
Abstract
Salidroside, a naturally occurring phenylpropanoid glycoside found in Rhodiola rosea, has shown
potential as a bioactive compound for enhancing cognitive function specifically memory, and
learning by promoting neurogenesis and protecting neural cells against damage. It is also known
to inhibit Death-Associated Protein Kinase 1 (DAPK-1), an enzyme critically implicated in
regulating apoptosis and autophagy. It is a multifunctional serine/threonine kinase that is highly
up regulated in Alzheimer’s disease (AD) and is also associated with several pathological
hallmarks of AD. In this investigation, we conducted a molecular docking study, which
demonstrated the substantial affinity and selectivity of salidroside towards DAPK-1.
Subsequently, we identified synthetic analogs of salidroside using PubChem databases, which
were subjected to docking studies to ascertain their binding affinities and ligand-target interactions.
Three salidroside analogs, CID-101530110, CID-101530061, and CID-101530061, were selected
for further evaluation. These analogs were subjected to comprehensive assessment, including
Absorption, Distribution, Metabolism, and Excretion (ADME) profiling using Swiss ADME and
toxicity analysis using the ProTox II server. The top hits were further analyzed using molecular
dynamics studies to elucidate their binding patterns. The findings of this study provide crucial
insights into the underlying molecular mechanisms and neuroprotective effects of salidroside and
its analogs. This knowledge holds promise for the development of DAPK-1 inhibitors as a potential
therapeutic approach for Alzheimer's disease.
Keywords: Salidroside, Alzheimer, DAPK1
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In-Silico Identification of the Potent Carotenoids Targeting the Kinase
Receptors for Cancer Therapy
Ressin Varghese, Kuntal Pal, Ashwni Kumar Cherukuri and Siva Ramamoorthy
Vellore Institute of Technology
siva.ramamoorthy@gmail.com
Abstract
Rationale: Cancer is considered the most malignant disorder with a predicted global burden of
about 30 million new cases by 2040, with the greatest increases happening in low- and middle-
income countries. Amidst of the sophisticated therapeutic strategies, the side effects caused by the
colossal use of cancer chemotherapeutics is a major concern. A rapid upsurge was observed in the
research of natural therapeutics for alleviating cancer during the past decades. In this scenario,
carotenoids are one of the most potent secondary metabolites derived from various natural
resources explored for targeted cancer therapy. We chose tryosine kinase receptors as our targeted
molecules owing to their key roles in signaling cascade regulating the growth, differentiation,
metabolism of cells, and apoptosis in response to various stimuli.
Materials and methods: A dataset was developed comprising of the FDA-approved standard
drugs and anticancer potential carotenoids categorized as carotenoids, apocarotenoids,
xanthophylls, and synthetic carotenoids through literature mining. A combined workflow of virtual
screening, molecular docking, and a supervised machine learning algorithm was applied to identify
the most potent carotenoid for kinase receptor-targeted cancer therapy. A predictive model was
developed employing ML to analyze the chemical feature similarity of selected carotenoids to
clinically approved positive cancer drugs. The best candidate identified for each receptor was
subjected to molecular dynamics simulation to assess the interaction stability.
Results: About fourty-two potential carotenoids were identified through literature mining and
were further screened by analyzing ADMET and toxicity. The potential carotenoids targeting
receptors HER2, HER3, EGFR, FGFR2, PDGFR, VEGFR2, ALK, RON kinase, MET compared
to FDA-approved drugs were further recognized through molecular docking and machine learning
approach. Four potential carotenoids including beta-carotene, canthaxanthin, neoxanthin,
peridinin, and fenretinide were identified through the combined molecular docking and machine
learning approach.
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Conclusion: A collective application of the carotenoid mixture for curing cancer will be a ground-
breaking and highly effective approach for treating cancer.
Keywords: Carotenoids, Cancer, Kinase receptors, Machine learning, Molecular docking
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Cigarette Butts: Tiny Nocuous particles inciting ecotoxicity via
oxidative stress in Pila virens
Koigoora Srikanth, Siksha Sharavani and Burri Ganesh
Dept of Biotechnology, VFSTR, Vadlamudi, Guntur District, Andhra Pradesh, India
koigooras@gmail.com
Abstract
Cigarette butts are exceedingly toxic substances in the aquatic environment. Snail's are the major
food source. In the aquatic (marine region) and also exposed to the chemical toxicity of the
cigarette butts. In order they can straightly affect the human being through food and contamination
take place in aquatic environment. In this novel study we evaluate the toxicity of cigarette butts on
gastropods and oxidative stress response. The cigarette butts solutions acquire by adding the five
butts in 1liter and soked it for 2 hrs and exposed the snail in different concentrations
(15%. ,25%,50%) control after exposing the snail for 48 hrs the. Mortality rate were recorded.
More over the 100% of organisms /snails were vanished in 50% concentrations in rest of solutions
the survival rate is longer compared to each different solution. Cigarette butts accumulation in
snail causes oxidative stress due to production ROS oxidative strees by Cigarette butts can damage
the digestive gland in snail. In snail and break down of dna and cause mutations consequently the
motive of this novel study was to inspect numerous amounts of toxicity effects of Cigarette butts
exposure including with oxidative stress
Keywords: Pila virens, Cigarette butts, Antioxidants, Oxidative stress
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Molecular Docking Study of Anti-Arthritic and Anti-Oxidant
Phytochemicals Identified from Indian Medicinal Plants Against
RANKL In the Treatment of Bone Degradation in Rheumatoid Arthritis
Devi Soorya Narayana S and Vino S,
Vellore Institute of Technology
svino@vit.ac.in
Abstract
Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease causing the destruction
of joints. Recent studies have found that osteoclasts play a significant role in bone degradation in
the disease. The tumor necrosis factor superfamily protein, osteoclast differentiation factor
receptor activator of NF-B ligand (RANKL) is important in osteoclast differentiation and bone
degradation in RA. RANKL activates and differentiates pre-osteoclasts and mature osteoclasts by
binding to their RANK receptors and the degree of growth and activity of the osteoclasts is
determined. Bone erosions in RA are caused by osteoclastic bone resorption in synovitis sites,
where RANKL expression is also noted. Furthermore, MRI bone edema in RA suggests the
existence of active inflammation inside bone as well as the presence of osteitis, which is likewise
related to the RANKL expression. Radiographic studies show that bone destruction in RA occurs
early and continues throughout the disease's duration. Bone erosion causes deformities of the
afflicted joints and inhibits patients' normal activities. Therefore, one of the most difficult
objectives in the therapy of RA is to prevent bone destruction.
Methodology: 3829 ligands were collected from literature, and IMPPAT (Indian Medicinal Plants,
Phytochemistry And Therapeutics) databases, which are phytocompounds with antioxidative
properties, and anti-arthritic properties. The structures were docked into the X-ray crystal structure
of the human RANKL-OPG complex (PDB ID: 3URF, R = 2.7 Å) after removing the OPG to free
the binding site, and chain A of sRANKL (162aa) was taken for further docking studies. The
CASTp server was utilized to identify the active site and the site-specific docking studies were
performed with AutoDock vina. The compounds were screened virtually by molecular docking
and further filtered using ADME properties. The CHARMM36 was used to generate the ligand
and protein coordinates of the best conformation. Concurrently, the protein complex was created
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using the Gromacs package. Protein-ligand complex trajectories were compared to confirm the
stability of the protein trajectory. The MD simulation for 200 ns was performed in a cubic water
box with TIP3 water molecules. The stability of the protein and protein-ligand complexes was
confirmed by comparing their RMSD, RMSF, hydrogen bond, Radius of Gyration. Additionally,
to evaluate the strength of the intermolecular interaction we calculate the binding free energies of
the complexes using the molecular mechanics Poisson Boltzmann surface area (MM-PBSA)
method. We performed MM-PBSA calculations through the gmx_mmpbsa python tool then the
results were analyzed using gmx_mmpbsa_ana.
Result and Discussion: 3829 ligands with inhibitory properties of RANKL, antioxidative, and
anti-arthritic properties were collected from the literature survey and IMPPAT database. The
AutoDock vina docking results revealed strong interaction between the phytocompounds and
RANKL and the best-docked compounds were 4-(P-Methoxyphenyl)-2-(4-phenyl-2-pyridyl)-6-
(2-pyridyl)-pyridine (-9.8 kcal/mol), Bismahanine (-9.2 kcal/mol), and Tingenone (-9.1 kcal/mol).
The MDS simulation study confirmed the stability of the complex.
Conclusion: The main goal of RA treatment is to avoid bone and joint deterioration and to keep
patients active on a daily basis with no treatment-associated side effects. Recent research has
shown that osteoclasts play a role in the etiology of bone and joint degradation in RA and can be
a powerful treatment target for the illness. Therapeutics that target osteoclast production or
function can, at the very least, slow the advancement of these bone alterations. Thus, the
phytocompounds retrieved in our study may act as a promising therapeutic option for RA with
great efficacy.
Keywords: Rheumatoid Arthritis, Bone degeneration, Phytochemicals
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Bioaugmentation Of Phenanthrene- Polycyclic Aromatic Hydrocarbon
Using Sphingomonas Species Isolated from The Petrol Bunk Soil
Prashanth Rajan A1, Biswanath Mahanty2
Department of Biotechnology, School of Bio Sciences and Technology Vellore Institute of
Technology, Vellore, Tamil Nadu, India1
Department of Biotechnology Karunya Institute of Technology and Sciences, Karunya Nagar,
Coimbatore, Tamil Nadu, India2
prashanth07052001@gmail.com
Abstract
Anthropogenic activities based on fossil fuels, coal gasification, coking industry, waste
incineration, oil spills, and other industrial processes have burdened the environment with
recalcitrant pollutant Polycyclic aromatic hydrocarbons (PAHs). Present worldwide abundant
distribution and long-term environmental persistence of PAH has become a global concern. PAH
are highly ecotoxic agents, also major mutagens and carcinogens in the present era, which
persuaded the United States Environmental Protection Agency (USEPA), to enlist phenanthrene
in the priority chemicals. Phenanthrene has fused rings and lacks a terminal surface for enzyme
activation making phenanthrene resistant to biodegradations resulting in accumulation through the
food chain. Phenanthrene reduces the growth and reproductive potential of aquatic life forms. It
causes respiratory problems, skin irritation, and cancer when seafood contaminated with
phenanthrene is consumed. The physical and chemical methods are highly expensive and
ineffective in removing PAHs in bioaugmentation. Based on these challenges the present study
was designed to isolate soil microbes from the contaminated sites. Soil samples from the Petrol
Bunks were collected to isolate Polycyclic Aromatic hydrocarbondegrading bacteria. The bacterial
isolates namely Isolate- M1S1, Isolate-M1S2, Isolate-M1S3, Isolate-M1S4, Isolate-M2S1, Isolate-
M2S2, Isolate-M2S3, Isolate-M2S4 were found to tolerate and degrade the PAHs. The isolates
showed good growth in phenanthrene-rich solution. Isolate-M1S3 had the highest efficiency in
phenanthrene degradation. This chemoheterotrophic yellowish bacterial colony was rod-shaped,
Gram-negative, aerobic, nonmotile, and non-spore-forming, which resembled the characteristics
of Sphingomonas species. This microbe has great potential to degrade the phenanthrene to
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nontoxic or low-toxic products. Bioaugmentation using this bacterium is economical and safer
compared to other contemporary existing technologies
Keywords: Polycyclic Aromatic Hydrocarbon, Phenanthrene, Bioaugmentation, Sphingomonas
species
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Nicotine Deter Women’s Reproductive Health: A Molecular Docking and
Dynamic Simulation Studies
Saranya G M, Gomathi. S, Poornimaa Murali, Ramanathan Karuppasamy, Rameshpathy
Manian,
Vellore Institute of Technology
mrameshpathy@vit.ac.in
Abstract
Over decades there is not much change in infertility and it still remains an on-going reproductive
problem. Lifestyle factor like smoking have been observed to be higher across the globe and found
to have a negative impact on fertility. Fertility in women is overseen by various hormones secreted
by different receptors found in the HPG (hypothalamus, pituitary gland) axis. Either inactivation
or activation of binding domains of these receptors by the ligand nicotine (NIC) will affect the
receptor stability or the signal transduction machinery. Molecular docking simulation was
achieved to NIC with receptors like LEPR, KISS1R, GnRHR, FSHR, LHR and ESTR by the
computational method. Almost in all the receptors interaction of NIC ligands shows the lowest
binding energy and hence confirms the effect of ligands in increasing the stability of the complex.
Among the selected six receptors, FSHR docked with NIC was found to be more endangered. It
shows the lowest binding energy (-7.21 kcal/mol), lowest inhibitor constant (0.005 μM), with
hydrogen bond interaction (ILE 111) and hence said to be the more stable complex with good
binding affinity. At the end, the conformational stability of the receptor-ligand complex in the
binding sites was examined using molecular dynamic (MD) simulation study for 50 ns simulation.
The results from MD simulation study highlights the stable binding of NIC with target receptor.
Thus, our in-silico results provide insights in the roles of these ligands, their possible binding
mode, their target contacts, efficiency and their specific interactions with the binding sites and
bridges the gap between theory and research in ways that were earlier impossible.
Keywords: FSHR, HPG axis, Infertility, Nicotine
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Decoding the Conformational Impact of PTEN Mutant R130Q on
Binding to the PIP2 Enriched Cell Membrane in Breast Cancer: A
Computational Approach
Rohit Karn, Prarthana Chatterjee, Arnold Emerson,
Vellore Institute of Technology
iarnoldemerson@vit.ac.in
Abstract
The phosphatase and tensin homolog (PTEN) is a tumor suppressor gene which encodes a dual-
specificity phosphatase. It serves as the key regulator of PI3K/AKT/mTOR signalling by
dephosphorylating the lipid phosphatidylinositol (3,4,5)‐triphosphate (PIP3) to
phosphatidylinositol (4,5)‐bisphosphate (PIP2). It is one of the most frequently mutating genes In
breast cancer. Here, we investigate the interaction of breast cancer PTEN mutant protein with the
cell membrane, in the presence of PIP2 and PIP3 molecules, using molecular dynamics
simulations. We also studied the role of C2 domain and C-terminal tail (CTT) in membrane
association. Two MD simulations (wild-type and mut_R130Q) of 500ns were performed using
Charmm-GUI and GROMACS. The full-length PTEN was modelled and subjected to mutation
during system preparation. Our study provides a mechanistic understanding of the interactions and
structural consequences of PTEN C-terminal tail and a comparison of mutant structure with wild-
type which broadens our insights into PIP2 and membrane interaction of PTEN. The Wild-type
PTEN protein interacts with the membrane in a better way than compared to the mutant which
possibly explains its tumor suppressor activity. This study provides insight into the function of
PTEN and its mutants towards disease pathogenesis.
Keywords: Breast cancer, PTEN, PIP2, Molecular dynamics simulation
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In Silico Analysis of Metabolites Produced by Lactic Acid Bacterial
(LAB) Cultures Against Aquatic Pathogens
Manishkumar1, Sudhakaran Raja*, Aquaculture Biotechnology Lab, Pearl Research Park,
School of Biosciences and Technology, Vellore Institute of Technology
2sudha@gmail.com , sudhakaran.r@vit.ac.in *
Abstract
Rationale: Aquatic pathogens pose a significant threat to aquaculture, causing substantial
economic losses and jeopardizing the sustainability of this important food production sector. These
pathogens can cause a range of diseases in farmed aquatic organisms, leading to mass mortalities,
reduced growth rates, and compromised product quality. Vibrio parahemolyticus and Vibrio
harveyi are Gram-negative bacteria, which is a major concern for aquaculture, specifically shrimp
farming, due to their ability to cause luminous vibriosis, a disease that affects a wide range of
marine animals including fish, crustaceans, and mollusks causing substantial economic losses and
posing a risk to global food security. White spot syndrome virus (WSSV), a double-stranded DNA
virus, is a devastating threat to the global shrimp aquaculture industry, causing catastrophic
economic losses which include 100% mortality within 3 to 7 days. In response to these threats,
researchers are exploring various strategies to control aquatic pathogens and protect aquaculture
production. These strategies include the use of probiotics, such as lactic acid bacteria (LAB), which
have been shown to have antimicrobial properties against aquatic pathogens.
Objectives: This study discusses the In-silico approach using metabolites produced by LAB
cultures against V. Parahemolyticus, V. Harveyi, and WSSV.
Materials and Methods: (1) Metabolites were extracted from LAB isolates, (2) GCMS analysis
was performed to confirm the metabolites present, (3) All the metabolites were docked against the
aquatic pathogens (Vibrio parahemolyticus, Vibrio harveyi, and White spot syndrome virus).
Results and Discussion: Results suggested that there are 11 metabolites produced by these LAB
isolates and Tris(2,4-di-tert-butylphenyl) phosphate produced by 2 out of 3 LAB cultures which
has better binding affinity compared to all other metabolites produced by all three LAB cultures.
This compound is also considered a common antioxidant in previous literature. This can help in
the future for a better understanding and proceeding to the animal study.
Keywords: Aquaculture, Aquatic pathogens, LAB cultures, Metabolites, and Molecular docking.
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BIXS Talks
Nipah Henipavirus Proteins Database
Basanth Geereddy1, Hiranyada Chintakrinda 1,3, Bhavya S Suravajhala 1,2
1. Bioclues.org, India.
2. Grade XI, Amrita Vidyalayam, Karunagappally, Kollam, Kerala. 690544
3. Grade XI, Kendriya Vidyalaya, Gachibowli, Telangana, India 500032.
sirinishu116@gmail.com2, bhavyas02007@gmail.com3
Abstract
Nipah virus (NiV), an emerging pathogen with a high fatality rate, poses a significant threat to
humans and livestock. We created the Nipah Henipavirus Proteins Database to address the need
for comprehensive information on this virus. Animals (such as bats or pigs) or contaminated foods
can transmit the Nipah virus to humans. Neither humans nor animals have been treated or
vaccinated against this virus. Researchers have developed experimental monoclonal antibodies to
treat Nipah virus disease under compassionate use. Supportive care is the primary treatment for
humans. Intensive supportive care is recommended for patients with severe respiratory and
neurologic complications. The RNA genome of Nipah henipavirus contains 9 proteins comprising
6 structural and 3 non-structural proteins. Studying and analyzing different virus proteins will help
us understand their mechanism and how they interact with our cells, which will further help us
develop antiviral drugs and practical tools to fight the virus. The Nipah henipavirus, which is the
causative organism of the Nipah virus, is a phosphoprotein belonging to the paramyxoviridae
family, which includes species that cause several other chronic diseases like measles, mumps, and
other respiratory tract infections. Nipah henipavirus is a single-stranded RNA virus made of six
consecutively arranged genes. The six polynucleotides/polypeptides found in a single strain of
this virus are Nucleocapsid, Phosphoprotein, Matrix, Fusion glycoprotein, Attachment
glycoprotein and long polymerase. The nucleocapsid, phosphoprotein, and polymerase are
responsible for forming the ribonucleoprotein of the virus. The fusion glycoprotein and attachment
glycoprotein are attached to the virion and play a vital role in the entry into the host cell. When the
virus enters the host's body, the proteases present in the host body’s immune system attack the
fusion glycoprotein and cleave it into two subunits called F1 and F2. The F1 subunit contains a
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fusion peptide that binds to the host’s cellular membrane and causes entry into the cell. The matrix
protein then initiates morphogenesis and budding. This causes virus replication in the host body,
and slowly, the virus takes control of the host’s immune system. Later on, due to the interactions
between the viral receptors of the host cell and the glycoprotein, some conformational changes are
triggered in the virus, activating the fusion glycoprotein that fuses to the cell membranes. The
pathogenicity of this virus is directly related to the intensity with which the virus fuses with the
receptors.
Keywords: Nipah virus, Bioinformatics, Database, Proteomics
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SCVDB: A Database of SARS-CoV-2 Annotated Proteins
Hiranyada Chintakrinda1,3, Bhavya Sri Suravajhala2,3
1. Grade XI, Kendriya Vidyalaya, Gachibowli, Telangana, India 500032.
2. Grade XI, Amrita Vidyalayam, Karunagappally, Kollam, Kerala. 690544
3. Bioclues.org, India.
sirinishu116@gmail.com1, bhavyas02007@gmail.com2
Abstract
SCVDB stands for SARS-CoronaVirus Database and Analytics
(https://bioclues.org/SCVDB/index.php ). SCVDB is a unique dynamic database that curates the
records that have been sourced primarily from UniProtKB and then manually curated. For any
internal conflicting or inconsistent annotation, SCVDB is aimed to be an integrated platform to
develop a database and analysis/tools. SCVBD is a bioclues project created during the lockdown
with the aim of inculcating and including school children in the subject area of bioinformatics.
Their enthusiasm warranted us to create a helpful resource Database with Sequences (Cov: 15 and
Cov2: 17) as a One-Point Portal (dated 07-Nov-2023). The importance of developing an integrated
platform with Sequences, Citations, Publications, and Clinical data warranted us in developing this
database. SCVDB contains 32 proteins; SARS-CoV-2 Data, NCBI got 5,113,401 SRA runs;
6,004,532 Nucleotide records; 8,097 ClinicalTrials.gov; 281,614 PubMed; and 376,649 PMC
presently. The SCVDB database mainly contains Catalogues that are lists of items. An integrated
database catalogue is a function embedded within a server that allows administrators to view
information for every database installed there. Additionally, the database catalogue stores metadata
on every database, like the number of rows and tables each database has. The catalogues in the
SCVDB database are: -
● Taxonomy: This catalogue spans through a set of Taxonomy Classification index
● Advance search: This catalogue allows users to search the SCVDB database through any set of
combinations of one or more fields with values.
● Keywords: This catalogue allows an end user to pick a set(s) of Keywords from the Index.
● Species: This catalogue enables the user to pick a set(s) of Species index
● Families: This catalogue allows users to choose a set(s) of Family index
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● Citation: This catalogue enables users to search the database through a Citation index [By Title,
Author, Journal, Year] in any combination(s).
Keywords: SARS-CoV-2, Acute respiratory syndrome, COVID-19, Lung infections, Database
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Video Abstracts
In Silico Analyses of Protein-Ligand Interactions Associated with Stress
Pathways in Plants
Praneet Prabhanjan B1, Nandni Kumari1 , Abilashni Arthiswaran1 , Dalia Vishnudasan1 , PB
Kavi Kishor2 , Ramesh Katam3 and Renuka Suravajhala1
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana, Kerala, India1
Department of Genetics, Osmania University, Hyderabad, India2
Florida Agricultural and Mechanical University, Gainsville, FL, USA3
ramesh.katam@famu.edu and renus@am.amrita.edu
Abstract
A vital biological mechanism that keeps life on earth alive is photosynthesis. Green plants, algae,
and some bacteria use this process to transform light energy into chemical energy. Proteins D1 and
D2 are essential elements of the Photosystem II (PSII) complex, a crucial part of the thylakoid
membrane found in chloroplasts. PSII is in charge of photosynthesis's first stage, which involves
absorbing light energy and utilizing it to start the electron transport process. In this work, we
performed a comprehensive investigation into the genetic variations within Taxus brevifolia,
Manihot esculenta, and Azadirachta indica. Our study focused on predicting gene functions,
exploring protein-protein interactions, and analyzing closeness, betweenness, and clustering
coefficients specifically related to chloroplast genes. Using a combination of various software tools
and biological databases. We identified significant factors associated with Photosynthesis II (PSII)
and explored the impact of herbicides and their analogs on PSII. By applying ADME-based
Lipinski rule properties screening with KNIME nodes and conducting molecular docking
experiments, we identified potential herbicide lead molecules that we firmly believe would help
enhance crop yield and mitigate weed growth, further contributing to advancements in agriculture
and sustainable food production.
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Unraveling the Genetic Basis of Prostate Cancer Phenotype in the
Indian Population
Bhargavi R1, Devendra Sharma2, Maneesh Vijayvargiya3, Rajaguru Aradhya1, Bipin G Nair1
and Prashanth Suravajhala1
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana, Kerala, India1,
Department of Urology, CK Birla Hospitals, Jaipur, Rajasthan, India2,
Department of Pathology, Mahatma Gandhi University of Medical Sciences and Technology,
Jaipur, Rajasthan, India3
prash@am.amrita.edu
Abstract
This study aims to explore the genetic variations associated with prostate cancer (PCa) in the
Indian population through Whole Exome Sequencing (WES) Methods: 39 Malignant PCa FFPE
samples with Gleason grade between 7-9 were collected from CK/Rukmani Birla hospital, Jaipur
with institutional ethics committee (IEC) clearance and informed consent from all participants was
duly obtained prior to the commencement of the study as part of our Cancer Prostate consortium
of India (CAPCI) efforts. WES was performed on an Novaseq 6000 Platform with the raw reads
then run through our in-house benchmarked variant calling pipeline, CONsensus Variant EXome
(CONVEX) which employs four different variant callers, viz. VarScan, bcftools call, vt and
Freebayes to obtain consensus variants of significance, after which gene annotation was done using
ANNOVAR/SnpEff. The list of common variants among all samples was then compared to the
missense deleterious genes obtained after benign subtraction earlier reported by Ravindran et al.,
2023 to obtain a list of common genes representative of both north and south indian population.
Furthermore, the list was compared to that of the OncoArray chip project from Prostate Cancer
Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL)
consortium which genotyped 140,000 PCa patients of European ancestry for 600,000 SNPs
associated to PCa and other cancers. Results: Our analysis revealed a spectrum of genetic
alterations with 2561 variants found to be consensus to all malignant samples of which 1669 were
identified as exonic variants, 724 intronic and 28 ncRNA variants. Among the exonic variants, 754
were identified as non-synonymous using ANNOVAR/SnpEff. Key cancer-related genes that were
consensus in our samples include CYP11B2, COL6A1, MYO15A, HEXB whose role in tumour
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invasiveness, angiogenesis and metastasis has been extensively studied. The comparison of our
list of consensus variants to the missense deleterious genes’ list reported by Ravindran et al., 2023
revealed six common genes, viz. ERV3-1, GPRIN2, MUC16, PHC1, UMODL1 and ERV3-1-
ZNF117 readthrough. Notable among these are MUC16 which is found to be overexpressed in
various cancers and promotes migration and invasiveness of cancer cells and ERV3-1, an
endogenous retroviral protein which could possibly serve as a prognostic marker. The comparison
of consensus variants from our data to OncoArray data from PRACTICAL consortium revealed
118 SNPs common between the datasets representing common SNPs between Indian and
European populations. Conclusions: The WES performed on PCa samples in our study revealed a
diverse genetic landscape of the Indian PCa phenotype, emphasizing the need for population-
specific studies to better understand the disease. The genetic variations identified in this study may
open new avenues for further research into their functional significance and potential as novel
diagnostic and prognostic markers.
Keywords: Prostate cancer, systems genomics, next generation sequencing, exome sequencing,
variants.
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Comparative Analysis of RNA-Seq Results in Parkinson's Disease:
Unraveling Molecular Insights into Disease Pathogenesis
Anu Sasidharan and Ekanath R
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana, Kerala, India
anusasidharanmay112000@gmail.com
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder that affects the motor system due to the
loss of dopaminergic neurons in the basal ganglia, a structure in the midbrain.This is an age related
diseases which has no complete cure. One of the reasons is that the disease mechanisms are not
fully understood, especially from a subcellular level. This study focuses on an in-depth analysis of
RNA-Seq data to compare the gene expression profiles between Parkinson's disease-affected and
normal conditions. We explore the vast RNA-Seq datasets, examining the gene expression profiles
in affected brain regions, and elucidate the dysregulated pathways and biological processes
associated with PD.This study contributes to a deeper comprehension of the molecular
underpinnings of Parkinson's disease and may facilitate the development of innovative treatments
and diagnostic tools for this devastating condition. By leveraging advanced bioinformatics tools
we aim to identify differentially expressed genes that could pinpoint potential biomarkers and
therapeutic targets that may aid in the development of innovative diagnostic tools and treatment
strategies. Additionally, we probe into the regulatory networks and noncoding RNAs that may play
pivotal roles in the pathogenesis of PD.
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Repurposing Targeted Therapeutics Against Monkeypox: In Silico
Modeling for the Upcoming Battle
Anguraj Moulishankar, Indrani Biswas, Rajul Jain, Kavyasudha Kanuparthi, Arsha Gopan,
Sivani Dinesh, Sneha Saji, Sneha Baiju, Sravyasree Gubbala, Renuka Suravajhala*,
Department of Pharmaceutical Chemistry, College of Pharmacy, Madras Medical College,
Chennai, India
Mahatma Gandhi Medical Advanced Research Institute (MGMARI),
Sri Balaji Vidyapeeth, Pondicherry,
India Bioclues.org, Hyderabad, India
Amrita School of Biotechnology, Kerala, India
renus@am.amrita.edu
Abstract
Background: The Monkeypox virus is considered the next pandemic by the World Health
Organization. The evolutionary history of Monkeypox has similarities to COVID-19. There is no
correlation between Monkeypox and SARS-COVID, but there is evidence of smallpox
disappearing and Monkeypox re-infections emerging. This suggests a common phylogenetic
origin. The study aims to screen antiviral drugs that target cellular and molecular factors important
for Monkeypox virus survival and maintenance.
Materials and Methods: Homology modeling was used to predict the structure of two proteins,
cell surface envelope protein (CSEVP) and DNA-dependent RNA polymerase (DpRNAp). The
predicted structure of the target protein was then used for molecular docking studies with antiviral
compounds. Additionally, the stability of potential lead complexes was simulated using the
GROMACS software collection on the two targets - CSEVP and DpRNAp - in complex with the
potential lead molecules - Rilpivirine and Tibo.
Results and Discussion: 70 compounds were tested against two targets, CSEVP and DpRNAp.
Idoruxidine had the highest binding affinity for both targets and exhibited significant anti-MPV
activity. Molecular dynamic simulation was also performed to assess the stability, conformational
changes, and binding interactions of the ligands with the protein.
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Matrix Metalloproteinases: Master Regulators of Tissue Morphogenesis
Sreesada P*, Vandana*, Bhagath Krishnan*, Amrutha R*, Yash Chavan*, Anjali*, Tanha*,
Parvathy Venugopal, Rajaguru Aradhya#, Bipin G Nair# *
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana PO 690525, Kerala,
India
rajagurua@am.amrita.edu and bipin@amrita.edu
Abstract
Matrix metalloproteinases (MMPs) belong to a class of zinc proteases that facilitate the
degradation of various components of the extracellular matrix (ECM). Beyond ECM degradation,
MMPs also influence processes such as inflammation, cell development, proliferation, and more.
In vivo genetic studies of Drosophila MMPs, specifically Mmp1 and Mmp2, have revealed their
pivotal role in tissue remodelling while not being critical for embryonic development (Page-
McCaw et al., 2003; Wen et al., 2020a). Both Drosophila MMPs exhibit the canonical and
conserved MMP domain organization (Lafever et al., 2017; Llano et al., 2002). A notable
distinction between Mmp1 and Mmp2 lies in their cellular localization; Mmp2 appears to be
membrane-anchored, while Mmp1 is released into the extracellular environment. This localization
difference underscores the significance of their roles within this small MMP family. MMPs
typically feature various domains, including the signal sequence, propeptide, catalytic domain, and
hemopexin-like domain. The signal sequence, also known as the pre-domain, guides MMP
production and directs it from the endoplasmic reticulum to the extracellular space. MMPs can be
broadly categorized into secretory MMPs and membrane-type MMPs (MTMMPs), with specific
subgroups characterized by their structure and function. The intricate functional diversity of human
MMPs poses challenges in analysing their intracellular activities, as the human genome encodes
approximately 23 distinct MMPs with overlapping functions. In contrast, the Drosophila
melanogaster genome encodes only two MMPs, dMMP1 and dMMP2 (Lafever et al., 2017).
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Unraveling the Clotting Cascade in Vitamin K Deficiency Associated
Comorbidities
Shalini Rajagopal, Jalaja Naravula and Pb Kavi Kishor
shalini.irajagopal@gmail.com
Abstract
The coagulation mechanism can be affected by vitamin K (VK) deficiency, a severe nutritional
imbalance that is associated with a number of comorbidities. In this study, we incorporate data
from the Gene Expression Omnibus (GEO) archive to provide a study into the unraveling of the
clotting cascade in the context of VK deficiency. Maintaining hemostasis is crucial, and VK is an
essential cofactor for the synthesis of coagulation factors. A lack of VK has been associated with
a higher risk of bleeding problems and other comorbidities. In the present investigation, we utilized
publicly accessible GEO datasets to examine the transcriptional alterations in genes linked with
the clotting cascade when deficiency of VK and its associated conditions are involved. The results
of our study suggested that there were significant variations in the expression of genes associated
with fibrinolysis, vascular homeostasis, and coagulation in response to VK deficiency. These
alterations were significantly connected to the risk of thrombotic and hemorrhagic episodes, as
well as various diseases that have been associated with VK deficiency-associated disease
phenotypes, including diabetes, placenta, prostate, hepatic, renal, and cardiovascular disorders.
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203
Inferring Variants of Uncertain Significance (VUS) in Rare Disease
Genetics: An India-Centric Study
Anjali Krishna A, Praveen Mathur, Krishna Mohan Medicherla, Renuka Suravajhala and Bipin
G Nair
anjalikrishnasree04@gmail.com
Abstract
Background/Research gaps: Genetic variation was believed to be associated with exonic regions
only. While much emphasis has been placed on coding regions of the genome, non-coding RNAs
(ncRNAs) have emerged as essential players in gene regulation and disease mechanisms.
Furthermore, genetic variation is beginning to be understood in ncRNAs. However, not much
studies have taken place on genetic variation attributing to ncRNAs in rare diseases. There are
approximately 6000 rare diseases in the world and over 1000 are reported in India. Many missense
and nonsense mutations are known, and variants of uncertain/unknown significance (VoUS) are
emerging to be understood for causing the rare diseases.
Objectives: In this work, we exploit meta-analyses and aim to identify genetic variation attributing
to rare diseases of the Asia/world and India. We also contemplate identifying variants specific to
Congenital Pouch Colon (CPC), an Anorectal malformation (ARM) that was largely studied and
further identify variants specific to syndromic/nonsyndromic ARM from samples sequenced and
analyzed in our lab.
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Materials and Methods: The genetic diversity inherent in human populations plays a pivotal role
in health and disease susceptibility. This study embarks on a comprehensive exploration of genetic
variants within three overarching population categories: Indian, Asian, and worldwide. The initial
step involved data acquisition from these databases, ensuring that the selected datasets provide an
accurate representation of genetic variation within each population category. The data were
collected from well-established genetic databases, ensuring inclusion of population-specific
information by employing Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) guidelines. Filters were applied to segregate variants present in the Indian, Asian, and
worldwide populations.To this end, we harness the data from two prominent sources, the
"indiGenomic VCF(Indian Genomic DB)" and "NCBI Rare Disease" databases, to illuminate the
genetic tapestry that unites individuals across diverse backgrounds. Functional impact assessment
was then employed to variants with potential biological significance and subsequently variants
were analyzed based on their minor allele frequency (MAF), with thresholds set to capture
common, rare, or population-specific genetic diversity. Population-specific MAF data were
utilized whenever available, offering insights into genetic variation tailored to each population.
Results and Discussion: We delved upon deciphering the candidate genes and their variants from
the Asian/world and Indian sub-population and mapped the non-coding variants precisely. The
common variants between Indigenomes and our annotation were searched and we found that there
are 155 common variants with 15 of them attributing to ncRNA variants, and 3 of those to be
pathogenic in Asian/world datasets. Whereas, 64 were commonly found variants with 5 of them
attributing to ncRNA and 2 of those classified as pathogenic. We also sought to check the common
variants or similar alleles attributing to rare diseases from our cohort. The results of this study
present a rich and diverse landscape of genetic variants within these three population categories.
The variants were visualized and interpreted with an emphasis on functional impact and allele
frequency distribution.
Conclusions: In conclusion, we explored common genetic variants across Indian, Asian/
worldwide populations underscoring the need for finding candidate variants attributing to
pathogenesis and ncRNAs. We believe an investigation into shared genetic diversity offers an
insightful glimpse into the human genetic commonality that transcends geographical boundaries
and ancestry.
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Inferring Recombination Events in SARS-CoV-2 Variants In Silico
Nihal Najeeb, Aparna B. Murukan, Anagha Renjitha, Malavika Jayaram, Ayisha A. Jabbar,
Haripriya Haridasan, Akshara Prijikumar, Sneha Baiju, Adrial Ann Nixon, Ponnambil Anantha
Krishnan, Sunu Rodriguez, Somesh Kumar, Sunil K. Polipalli, Keshav K. Singh, Bipin G. Nair,
Sudeep D. Ghate, R. Shyama Prasad Rao, Polavarapu Bilhan Kavi Kishor, Arya Aloor, Renuka
Suravajhala, Gyaneshwer Chaubey, and Prashanth Suravajhala
aparnamurukan448@gmail.com
Abstract
Over the last 34 months, at least 10 severe acute respiratory syndrome coronavirus 2 (SARSCoV-
2) distinct variants have evolved. Among these, some were more infectious while others were not.
These variants may serve as candidates for identification of the signature sequences linked to
infectivity and viral transgressions. Based on our previous hijacking and transgression hypothesis,
we aimed to investigate whether SARS-CoV-2 sequences associated with infectivity and
trespassing of long noncoding RNAs (lncRNAs) provide a possible recombination mechanism to
drive the formation of new variants. This work involved a sequence and structure-based approach
to screen SARS-CoV-2 variants in silico, taking into account effects of glycosylation and links to
known lncRNAs. Our inquiry commences from the genesis of the alpha variant in December 2019,
aiming to discern the occurrence and mechanisms of recombination events. Additionally, we
explore the impact of these events on the transcriptional repertoire. An intriguing observation
emerges as not all variant sequences exhibit uniform length, prompting an inquiry into the
regression pathway of long noncoding sequences. This investigation seeks to unravel why SARS-
CoV-2 demonstrates a proclivity towards specific lncRNAs, pinpointing their precise locations
and assessing the likelihood of recombination events. Furthermore, we postulate that when a host
is exposed to diverse SARS-CoV-2 variants, co-infection may transpire, potentially resulting in
the amalgamation of genetic material. This phenomenon, intrinsic to the virus's self-defense
mechanism, fosters resistance and genetic diversity. Through the amalgamation of modern
bioinformatics tools, including Pangolin, ITOL, NCBI, NONCODE, and Datawrapper, we aim to
glean compelling insights. Our pilot study has yielded key candidate sequences associated with
SARS-CoV-2, employing similarity and dissimilarity approaches to elucidate vivid sequences
pertinent to recombination and transgression pathways. Taken together, the findings suggest that
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transgressions involving lncRNAs may be linked with changes in SARS-CoV-2–host interactions
driven by glycosylation events.
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Applications of Explainable AI in Bioinformatics
Nandini Gandhi, Flame University,
nandi2608@gmail.com
Abstract
Explainable AI (XAI) involves techniques and methods aimed at providing clear, human-
understandable explanations for AI and machine learning model decisions. It addresses the opacity
of black-box models, offering transparency in how AI systems reach conclusions. Interpretable
Machine Learning models can clarify their prediction process and the factors influencing their
outcomes. Artificial intelligence (AI) systems, using advanced neural networks and machine
learning (ML) algorithms, are crucial for solving complex issues in bioinformatics, biomedical
informatics, and precision medicine. However, the complexity of ML models, often viewed as
intricate and opaque, can make it hard to understand how they make decisions. This lack of
transparency is a challenge for users, decision-makers, and AI developers. In healthcare, where
lives are at stake, it's not only important but also legally required for AI systems to be clear and
accountable. Another concern is fairness, as algorithmic decisions should be unbiased and not
discriminate based on sensitive attributes. Explainable AI (XAI) steps in to address this, aiming to
uncover how black-box models work and reveal how AI systems reach their conclusions.
Interpretable ML models can clarify their prediction process and identify the factors that influence
their results. Bioinformatics has seen a rise in the use of Explainable Artificial Intelligence (XAI),
which offers solutions to complex issues and increases decision-making process transparency.
Protein structure prediction, genomic sequence analysis, gene-finding, DNA sequencing, gene
expression analysis, genome annotation, computer-aided drug design, disease biomarker
identification, biological system modeling, drug interaction prediction, and patient phenotyping
are just a few of the applications for XAI. However, since biomedical data is complicated and
nonlinear, using XAI in bioinformatics poses unique challenges. Despite these difficulties, case
studies in text mining, bioimaging, and cancer genomics have demonstrated the potential of XAI
techniques to increase transparency. There are many models of explainability in XAI. For instance,
two popular models include LIME (Local Interpretable Model-agnostic Explanations) and SHAP
(Shapley Additive Explanations), play a crucial role in demystifying the decision-making process
of complex machine learning models. LIME operates by generating locally faithful explanations,
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providing insights into a model's predictions for a specific instance by approximating it with a
simpler, more interpretable model. This allows humans to grasp the reasoning behind a particular
prediction. On the other hand, SHAP employs Shapley values from cooperative game theory to
attribute a portion of the model's prediction to each feature, highlighting their individual impact.
By assigning a quantitative measure of importance, SHAP enables a comprehensive understanding
of feature contributions, aiding in identifying critical factors influencing model outputs. Both
LIME and SHAP are instrumental tools in building trust and transparency in AI systems, making
them invaluable for applications where model interpretability and accountability are paramount.
In this work, we plan to review the existing literature on Explainable AI and also provide our own
case studies employing explainable AI methodology in bioinformatics
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Development of a Web-Resource for Prioritizing Mutations from Next
Generation Sequencing Data Using an Agnostic Framework
Anandhu Presannan, Nidheesh M and Bipin G Nair
anandhupresannan@am.amrita.edu
Abstract
Background/Research: Next generation sequencing (NGS) technologies have enabled the
identification of genetic variants associated with various diseases. However, the annotation and
prioritization of these variants is challenging, especially for non-coding regions and rare diseases.
Existing databases and tools rely on external information and may not capture the pathogenicity
of novel or population-specific variants. Therefore, there is a need for a web resource that can
prioritize mutations from different NGS data using an agnostic framework, which is independent
of predictions from generic databases.
Objectives: The main objectives of this proposal are: • To develop a web-resource with an agnostic
framework for ascertaining candidate mutations from vivid NGS data. • To validate the
agnostic/prioritization framework with various diseased phenotypes using machine learning
heuristics.
Materials and Methods: The proposed methodology consists of the following steps: • Data
collection: The data for this study will be taken from the already existing WES/NGS datasets
available in public domain and from the in-house NGS datasets from various human diseased
phenotypes that have been studied by the authors. • Data screening: All mutations with minor allele
frequency (MAF) <=0.05 and a minimum depth of coverage of 5 will be judiciously screened as a
training dataset. A set of mutations for the test will be taken so that validation will be done followed
by precision and recall. • Data integration: The screened mutations will be integrated with other
genomic and transcriptomic data, such as long non-coding RNAs (lncRNAs), microRNAs (miRs),
and differentially expressed genes (DEGs), to identify the regulatory elements and interactions
associated with the diseases. • Data analysis: The integrated data will be analysed using systems
genomics and machine learning approaches, to develop a prediction model for prioritizing
mutations using an agnostic framework. The model will be based on various features, such as
conservation, functional impact, network properties, and subcellular localization. • Data
validation: The prediction model will be validated using the test dataset and compared with the
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existing databases and tools, such as Clinvar, CADD, and GERP. The validation will also follow
the American College of Medical Genetics and Genomics (ACMG) and Kaviar guidelines. • Web
server development: The prediction model will be implemented as a web server using Apache/CGI
generic with custom options and instantiation of codes. The web server will allow the end-users to
upload their NGS data and obtain the prioritized mutations with detailed annotations. Results and
Discussion: The expected results of this study are: A comprehensive marker catalogue specific to
mutations and from end-users whether or not any mutations are pathogenic in nature. The standard
operating procedure (SOP) for validating mutations using an agnostic framework. A user-friendly
web server for prioritizing mutations from varied NGS data.
Conclusions: The proposed study aims to develop a web-resource for prioritizing mutations from
different NGS data using an agnostic framework. In this study we employed integrated systems
genomics and machine learning approaches to validate the agnostic/prioritization framework with
various diseased phenotypes. The study will provide a useful tool for the researchers and clinicians
to identify the pathogenic mutations and their regulatory mechanisms.
Keywords: web-resource, mutations, next generation sequencing, agnostic framework, machine
learning
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A Holistic Perspective on Host-Pathogen Interactions in Different
COVID-19 Severity Levels Through an Integrated Omics Analysis
Mairembam Stelin Singh1, Mairaj Ahmed Ansari2, Sailu Yellaboina3
Department of Biochemistry, Jamia Hamdard, New Delhi, India1,
Department of Biotechnology, Jamia Hamdard, New Delhi, India2,
All India Institute of Medical Sciences, Bibinagar, Hyderabad, Telangana, India3
mairembam.stelin@rgu.ac.in
Abstract
The global SARS-CoV-2 pandemic, responsible for millions of deaths worldwide, has raised
complex questions about the pathogenesis of COVID-19. The diverse clinical outcomes, ranging
from mild to severe disease, remain inadequately understood. To shed light on these mysteries, a
novel approach integrating host-pathogen protein interactions and virus-induced host gene
expression data was employed. A thorough examination of RNA-Seq data from 1960 samples,
originating from 12 projects encompassing various disease severities, revealed genes with
differential expression in mild, moderate, and severe COVID-19 cases.
Remarkably, when delving into the pathways influenced by the 49 SARS-CoV-2 proteins within
the host, a strong connection emerged with processes linked to ribosomal biogenesis, translation,
and translocation. An intriguing pattern unfolded: these pathways and related cellular components,
such as ribosomal biogenesis and translation, were upregulated in mild cases but downregulated
in severe conditions. This suggests that in the face of severe COVID-19, the host's response
involves the shutdown of translation pathways targeted by the virus, potentially inhibiting viral
replication. However, this strategy might come at a cost, possibly compromising vital host cellular
functions, including protein synthesis and the host's capacity to mount an effective antiviral
response, with broader implications for human health and well-being.
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Semantic Retrieval of Antimicrobial Resistance Information using
Natural Language Processing and Deep Learning
Swattik Biswas and Smriti Kandhadai
swattik881@gmail.com
Abstract
Antimicrobial resistance (AMR) is the ability of microorganisms to withstand the effects of
antimicrobial drugs, such as antibiotics. It is a growing public health concern that threatens the
effectiveness of these drugs and can lead to serious infections that are difficult to treat. Traditional
methods of retrieving information from research articles on AMR are often time-consuming and
rely on manual curation. To overcome these limitations, researchers are exploring ways to use
natural language processing (NLP) and machine learning techniques. The objective of this project
is to develop a system to automatically extract and retrieve antimicrobial resistance information
from research articles. The model will use NLP techniques to identify and understand the semantic
meaning of the text, and then use this information to retrieve relevant information. This approach
leverages a combination of NLP techniques, including named entity recognition, entity linking,
and semantic similarity measures. By analyzing the textual content of research articles, the system
is capable of identifying key entities such as antimicrobial agents, resistant strains, and associated
genes. Furthermore, it can establish relationships between these entities, providing valuable
insights into the underlying mechanisms of antimicrobial resistance. By automating the process of
information extraction from research articles, the project can also save time and resources for
researchers and healthcare professionals.
Keywords: Deep Learning, Antimicrobial resistance, Natural Language Processing
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AI-Generated Synthetic Genes for Investigating Novel Biomarkers of
Systemic Lupus Erythematosus
Peehoo Gaur
IIS School,Jaipur
amita.1983@iisuniv.ac.in
Abstract
Systemic Lupus Erythematosus (SLE) is a complex autoimmune disorder that manifests with a
wide range of clinical symptoms and immune system irregularities, largely impacting women from
15-45 years. The timely identification and intervention are paramount in effectively managing
SLE. Unfortunately, the deficiency of robust research materials often hampers this process. The
intricate interplay among genetic predisposition, environmental influences, and hormonal factors
contributes to the onset and progression of the disease. Some genes exhibit a latent risk, like
TNIP1, TNFAIP3, etc, akin to a ticking time bomb, necessitating their early identification for
proactive measures in preventing SLE. Genomics, a branch of omics, has significantly contributed
to the accumulation of large-scale data that fuels these investigations. Machine learning has further
enabled the integration and analysis of omics data, leading to the discovery of new biomarkers
with potential applications in disease prediction, patient stratification, and precision medicine. In
past few years, Generative AI, including pre-trained models, is gaining traction in omics studies,
drawing parallels between language constructs and cellular biology for transformative research in
genetics and cellular biology. These models have been employed to create artificial protein and
drug molecules in various research studies. In 2019, Periasamy and Byrd conducted a phenotype
study utilizing Generative Adversarial Network (GAN) technology to analyze images of butterfly
rashes associated with SLE. However, there is currently no evidence of the application of such
techniques for the generation of synthetic genes in the context of SLE.
Methodology: We aimed to develop a prototype model for gene generation using (GAN) model
by employing gene sequences. Eight genes TNIP1, TNFAIP3, RASGRP3, TNXB, TLR7, ATG5,
ITGAM and STAT4, known as key contributors to SLE in both Asian and European populations,
were selected for designing of the GAN model. The gene sequences were obtained from UniProt.
The sequence length varies between 279 and 4314 in the dataset. The GAN model was
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implemented in Python on the Google Colab platform. The study made use of essential libraries,
including TensorFlow, Pandas, and Keras.
Results and Future Work: Through this model, we successfully generated 5 synthetic gene
sequences. These synthetic genes can be subjected to further examination, as they may present a
valuable opportunity for conducting comprehensive studies on protein synthesis stemming from
these genes. Additionally, they may lead to the discovery of novel biomarkers and facilitate the
exploration of their interactions with pharmaceutical compounds.
INBIX’ 23
Editorial Team
Devi Soorya Narayana S
Tejeswini B
Karishma Sahoo
HemaNandini. R. K
Poornimaa. Mu
Premkumar T
Discover the forefront of bioinformatics in healthcare at our Inbix-2023 at VIT,
where a convergence of experts, researchers, and students from across the globe
promises a dynamic exchange of knowledge. Esteemed speakers will illuminate
the latest advancements, providing invaluable insights into the transformative
role of bioinformatics in shaping the future of healthcare. This gathering serves
as a nexus for professionals to connect, fostering collaborative endeavors that
transcend geographical boundaries. From groundbreaking research
presentations to interactive sessions, the conference aims to unravel the
complexities of bioinformatics, offering a platform for intellectual exchange and
the cultivation of innovative ideas.
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